Kernelized Multiview Projection for Robust Action Recognition

Abstract

Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP’s performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques.

Introduction

Human action recognition has been widely applied to human-computer interaction, human behavior analysis, video surveillance, robotics and so on. Traditional action recognition techniques are mainly based on single feature representations, either global (Shao et al. 2014) or local (Laptev et al. 2008). For local feature extraction, an unsupervised detection technique, such as: cuboid detector (Dollár et al. 2005), is first applied to locate the spatio-temporal interest points around which the salient features, e.g., histogram of 3D oriented gradients (3DHOG) (Klaser and Marszalek 2008), 3D scale invariant feature transforms (3DSIFT) (Scovanner et al. 2007), or histogram of optical flow (HOF) (Laptev et al. 2008), are extracted. Then, the bag-of-visual-words scheme is employed to embed these local features into a whole histogram representation. On the one hand, local feature based methods tend to be more robust and effective in challenging scenarios, while this kind of representation is often not precise and informative because of the quantization error during codebook construction and the loss of structural relationships among local features. On the other hand, global representations (Bobick and Davis 2001; Ji et al. 2013; Taylor et al. 2010) describe the action clip as a whole. Thus, it would be more informative to capture the discriminative features along both spatial and temporal dimensions. Unfortunately, global methods are sensitive to shift, scaling, occlusion, and cluttering, which commonly exist in action sequences.

Notwithstanding the remarkable results achieved by both local and global methods in some cases, most of them are still based on single feature representations. Since variations in lighting conditions, intra-class differences, complex backgrounds and viewpoint and scale changes all lead to obstacles for robust feature extraction and action classification, single feature representations cannot handle the realistic tasks to a satisfactory extent. In some situations, the direct concatenation of different features such as (Wang et al. 2013) can improve the performance over single features. However, the concatenation will make the representation quite lengthy and the relationship between different features is not exploited.

In practice, a typical action clip can be represented by different views/features, e.g., gradient, shape, color, texture and motion. Generally speaking, these views from different feature spaces always maintain their particular statistical characteristics. Accordingly, it is desirable to incorporate these heterogeneous feature descriptors into one compact representation, leading to the multiview learning approaches (Long et al. 2008; Xia et al. 2010; Xu et al. 2014, 2015). These techniques have been designed for multiview data classification (Zien and Ong 2007), clustering (Bickel and Scheffer 2004) and feature selection (Zhao and Liu 2008). For such multiview learning tasks, the feature representations are usually very high-dimensional for each view. However, little effort has been paid to learning low-dimensional and compact representations for multiview computer vision tasks. Thus, how to obtain a comprehensively low-dimensional embedding to discover the discriminative information from all views is a worthy research topic, since the effectiveness and efficiency of the methods drop exponentially as the dimensionality increases, which is commonly referred to as the curse of dimensionality.

In this paper, we propose to encode different feature representations for action recognition using a novel multiview subspace learning method called Kernelized Multiview Projection (KMP). Our preliminary study shows KMP can produce outstanding results for image classification (Yu et al. 2015). For action recognition, the spatio-temporal nature of a video sequence has to be considered and represented in a meaningful manner. Particularly, each action clip is first described by several individual views using frame-based representations, which contain the whole human body with the complete information of spatial structure and share the advantages with the global representation methods. Therefore, the adopted representation can be regarded as a semi-holistic representation of human actions. It inherits the advantages of global features in the spatial dimension and meanwhile has the superiority of local features in the temporal axis. To further preserve the sequential information of actions (Zhang and Tao 2012), for each view, the dynamic time warping (DTW) (Berndt and Clifford 1994) technique is applied to form radial basis function (RBF) sequential kernels. Having obtained kernel values for each view in the reproducing kernel Hilbert space (RKHS), KMP is able to fuse the features from different views, which have different dimensions, by exploring the complementary property of different views and finally finds a unique low-dimensional subspace where the distribution of each view is sufficiently smooth and discriminative. Different from multiple kernel learning methods (Gönen and Alpaydin 2011) which include linear and nonlinear approaches to learn the fused kernel matrix based on the maximum margin criterion, KMP also investigate the similarity and local information of features from each view.

The rest of this paper is organized as follows. In Sect. 2, we give a brief review of the related work. The details of our method are described in Sect. 3. Section 4 reports the experimental results. Finally, we conclude this paper in Sect. 5.

Related Work

A simple multiview embedding framework is to concatenate the feature vectors from different views together as a new representation and utilize an existing dimensionality reduction method directly on the concatenated vector to obtain the final mulitiview representation. Nonetheless, this kind of concatenation is not physically meaningful because each view has a specific characteristic. And, the relationship between different views is ignored and the complementary nature of intrinsic data structure of different views is not sufficiently explored.

One feasible solution is proposed in (Long et al. 2008), namely, distributed spectral embedding (DSE). For DSE, a spectral embedding scheme is first performed on each view, respectively, producing the individual low-dimensional representations. After that, a common compact embedding is finally learned to guarantee that it would be similar with all single-view’s representations as much as possible. Although the spectral structure of each view can be effectively considered for learning a multiview embedding via DSE, the complementarity between different views is still neglected.

To effectively and efficiently learn the complementary nature of different views, multiview spectral embedding (MSE) is introduced in (Xia et al. 2010). The main advantage of MSE is that it can simultaneously learn a low-dimensional embedding over all views rather than separate learning as in DSE. Additionally, MSE shows better effectiveness in fusing different views in the learning phase.

However, both DSE and MSE are based on nonlinear embedding, which leads to a serious computational complexity problem. In particular, when we apply them to classification or retrieval tasks, the methods have to be re-trained for learning the low-dimensional embedding when new test data are used. Besides, this kind of mechanism causes an uncertain training phase, since the low-dimensional representations of training data are always changing after retraining the model for a new test sample. Due to their nonlinearity nature, this will cause heavily computational costs and even become impractical for realistic and large-scale scenarios.

Therefore, in this paper, we propose a robust linear projection embedding method for RKHS, namely, KMP. It is noteworthy that, different from non-linear approaches, once the learning phase of KMP is finished and the projection is learned, it will be fixed and can be directly used to embed the new test samples without any re-training (Fig. 1).

Fig. 1

Illustration of selected middle frames from actions “Handwaving” and “Diving”

Methodology

Our recognition system is composed of the following main stages: (1) Pose description: For each video sequence, a set of visual features is extracted from each frame to represent the pose appearing in it. (2) Sequential distance kernel learning: Each feature view is computed into a kernel matrix via our proposed Gaussian-sequential learning. (3) Kernelized Multiview Projection: KMP is able to successfully explore the complementary property of different views and finally finds a discriminative low-dimensional subspace to fuse all views into a single feature vector. (4) Action recognition: the SVM with the RBF kernel is finally applied to categorize actions into different classes. The flowchart of the proposed method is illustrated in Fig. 2. We will detail the above stages in the following sections.

Fig. 2

Working flow of the proposed method. Multiple features are extracted from training video data for each frame. Based on the data after incremental naive Bayes denoising, the dynamic time warping is performed to construct the kernel matrices for each view. Then a projection matrix and weights for kernel matrices are derived by an EM-like alternate optimization procedure

Notations

We are given N training video sequences $\{v_1, \ldots , v_N\}$ and M different descriptors are used for multiview feature extraction. For the i-th view and p-th video sequence, $X^i_p$ represents the matrix composed of the feature column vector of i-th view in time-sequential order. Since the dimensions of various descriptors are different, kernel matrices $K_1, \ldots , K_M \in \mathbb {R}^{N \times N}$ are constructed in Sect. 3.3 for the fusion of different views. Our task is to output an optimal projection matrix $P \in \mathbb {R}^{N \times d}$ and weights $\{\alpha _1, \ldots , \alpha _M\}$ ($\sum ^M_{i=1} \alpha _i =1$) for kernel matrices such that the fused feature matrix $Y = [\mathbf {y}_1, \ldots , \mathbf {y}_N]^T = KP = (\sum ^{M}_{i=1} \alpha _i K_i)P$ can represent original data comprehensively.

Incremental Naive Bayes Denoising

In a video sequence, however, not all of the poses are informative and discriminative for action recognition. Some poses may carry neither complete nor accurate information and would even contain common patterns shared by various action types. Since these poses in a video sequence cannot represent the action well and would cause confusion during the classification phase, a weakly supervised method, termed incremental Naive Bayes filter (INBF), has been carried out to filter the noisy representation and keep the relatively representative and discriminative poses, i.e., the key poses.

For each action category, ten action sequences are randomly selected. We choose a small set of discriminative poses for a certain action type from each action sequence as the INBF initial positive samples (labeled as $y=1$), and the remaining frames are adopted as the negative ones ($y=0$). As illustrated in Fig. 1, the five frames in the middle of an action sequence are selected as discriminative poses. We repetitively apply the above procedure to each action type. INBF is then regarded as an unsupervised online learning strategy.

For the i-th feature view, the representation of each pose (frame) s is $\mathbf {x}^{i}(s) = (x^{i}_{1}(s), \ldots , x^{i}_{D}(s)) \in \mathbb {R}^{D}$. Since all the features we extracted are based on statistical histograms, we assume all elements in $x^{i}$ are independently distributed and model them with a naive Bayes classifier:

$$\begin{aligned} \begin{aligned} P(\mathbf {x}^{i})&=\log \frac{\varPi _{m=1}^{D}\Pr (x^{i}_{m}|y=1) \Pr (y=1)}{\varPi _{m=1}^{D}\Pr (x^{i}_{m}|y=0) \Pr (y=0)}\\&=\sum _{m=1}^{D}\log \frac{\Pr (x^{i}_{m}|y=1)}{\Pr (x^{i}_{m}|y=0)}.\\ \end{aligned} \end{aligned}$$

(1)

Note that we make the assumption of a uniform prior, i.e., $\Pr (y = 1)=\Pr (y = 0)$, and $y\in \{0,1\}$ is a binary variable which represents the positive and negative sample labels, respectively.

Furthermore, in either statistics or physics, real-world data distribution empirically follows the same form, i.e., Gaussian distribution. Thus, the conditional distributions $x^{i}_{m}|y=1$ and $x^{i}_{m}|y=0$ in the classifier $P(\mathbf {x}^i)$ are assumed to be Gaussian distributed with the four-tuple $(\mu ^{m}_{y=1}, \mu ^{m}_{y=0}, \sigma ^{m}_{y=1}, \sigma ^{m}_{y=0})$, which satisfy

$$\begin{aligned} x^{i}_{m}|y=1\thicksim N\left( \mu ^{m}_{y=1},\sigma ^{m}_{y=1}\right) \end{aligned}$$

and

$$\begin{aligned} x^{i}_{m}|y=0\thicksim N\left( \mu ^{m}_{y=0},\sigma ^{m}_{y=0}\right) . \end{aligned}$$

Up to now, for a certain feature view, we can initialize a group of naive Bayes models for each action type, and the training sequence is successively employed through all the models. The Gaussian parameters in INBF can be then incrementally updated as follows:

$$\begin{aligned} \begin{aligned}&\mu ^{m}_{y=1}\leftarrow \lambda \mu ^{m}_{y=1}+(1-\lambda )\mu _{y=1},\\&\sigma ^{m}_{y=1}\leftarrow \sqrt{\lambda \left( \sigma ^{m}_{y=1}\right) ^{2}+(1-\lambda )(\sigma _{y=1})^{2} + \lambda (1-\lambda ) \left( \mu ^{m}_{y=1}-\mu _{y=1}\right) ^2}, \end{aligned} \end{aligned}$$

(2)

where $\lambda >0$ denotes the learning rate of INBF, $\mu _{y=1} = \frac{1}{S}\sum _{s|y(s)=1}x^{i}_{m}(s)$, $\sigma _{y=1} = \sqrt{\frac{1}{S}\sum _{s|y(s)=1}(x^{i}_{m}(s)-\mu _{y=1})^{2}}$ and $S = |\{s|y(s)=1\}|$. And $\mu ^m_{y=0}$ and $\sigma ^m_{y=0}$ have similar update rules. The above solutions are easily obtained by maximum likelihood estimation. In this way, we can use INBF to keep the representative frames for the later learning phase and discard irrelevant frames to decrease the influence of noise.

RBF Sequential Kernel Construction

For the i-th view, since we extract features from the frames of video sequences, each video sequence can be described by a set of features with a sequential order (along the temporal axis). The similarity between video $v_p$ and video $v_q$ under view i: $k_i(v_p, v_q)$ can be measured via DTW (Berndt and Clifford 1994). Therefore, the kernel function can be defined as: $k_i(v_p, v_q) = \exp (-\frac{DTW(X^i_p, X^i_q)^2}{2\sigma ^{2}})$, where $DTW(X^i_p, X^i_q)$ indicates the sequential distance computed via DTW and $\sigma$ is a standard deviation in the RBF kernel. In this way, we can easily obtain the kernel matrices for different views using the above equation.

Fig. 3

Illustration of the similarity matrix construction

Kernelized Multiview Projection

Based on the above kernel construction, we can obtain kernel matrices $K_1, \ldots , K_M \in \mathbb {R}^{N \times N}$ with the same size for M views with different dimensions. Furthermore, we use the label of training video sequences to supervise the calculation of the similarity matrix $W_i$ for the i-th view. Then each component of $W_i$ is computed as follows:

$$\begin{aligned} (W_i)_{pq}= \left\{ \begin{array}{ll} \exp \left( -\frac{DTW(X^i_p, X^i_q)^2}{2\sigma ^{2}}\right) , &{} C(p)=C(q) \\ 0, &{} otherwise \end{array} \right. , \end{aligned}$$

(3)

where C(p) is the label function which indicates the label of video $v_p$ and $p, q = 1, \ldots , N$. In fact, the similarity matrix $W_i$ is a block matrix consisting of some submatrices of kernel matrix $K_i$ as illustrated in Fig. 3. Then we have the diagonal matrix $D_i$ in which $(D_i)_{pp} = \sum _q (W_i)_{pq}$ and the Laplacian matrix $L_i = D_i - W_i$ for each view i.

Due to the complementary nature of different descriptors, we assign different weights for different views. The goal of KMP is to find the basis of a subspace in which the lower-dimensional representation can preserve the intrinsic structure of original data. Therefore, we impose a set of nonnegative weights $\alpha = (\alpha _1, \ldots , \alpha _M)$ on the similarity matrices $W_1, \ldots , W_M$ and we have the fused similarity matrix $W = \sum ^M_{i=1} \alpha _i W_i$ and the fused Laplacian matrix $L = \sum ^M_{i=1} \alpha _i L_i$.

For the kernel matrix, since we use the same method (DTW) to compute kernel values and similarities, we can also define the fused kernel matrix $K= \sum ^M_{i=1} \alpha _i K_i$. In fact, suppose $\phi _i$ is the substantial feature map for kernel $K_i$, i.e., $K_i = \phi _i(X^i)^T \phi _i(X^i)$, then the fused kernel value is computed by the feature vector concatenated by the mapped vectors via $\phi _1, \ldots , \phi _M$, since we have

$$\begin{aligned} \begin{aligned} K&= \sum ^M_{i=1} \alpha _i K_i = \sum ^M_{i=1} \alpha _i \phi _i(X^i)^T \phi _i(X^i) \\&= \left[ \begin{array}{c} \sqrt{\alpha _1} \phi _1(X^1) \\ \vdots \\ \sqrt{\alpha _M} \phi _M(X^M) \\ \end{array} \right] ^T \left[ \begin{array}{c} \sqrt{\alpha _1} \phi _1(X^1) \\ \vdots \\ \sqrt{\alpha _M} \phi _M(X^M) \\ \end{array} \right] \\&= \phi (X)^T \phi (X), \end{aligned} \end{aligned}$$

where $\phi (\cdot ) = [\sqrt{\alpha _1} \phi _1(\cdot )^T, \cdots , \sqrt{\alpha _M} \phi _M(\cdot )^T]^T$ is the fused feature map and $X = (X^1, \ldots , X^M)$ is the M-tuple consisting of features from all the views.

To preserve the fused locality information, we need to find the optimal projection for the following optimization problem:

$$\begin{aligned} \mathop {\text {arg min}}\limits _{\mathbf {v}} \sum _{ij} \Vert \mathbf {v}^T \psi _i - \mathbf {v}^T \psi _j\Vert ^2 (W)_{ij}, \end{aligned}$$

(4)

where $\psi _i$ is the fused mapped feature, i.e., $[\psi _1, \ldots , \psi _N] = \phi (X)$. Through simple algebra derivation, the above optimization problem can be transformed to the following form:

$$\begin{aligned} \mathop {\text {arg min}}\limits _{\mathbf {v}} \hbox {Tr}(\mathbf {v}^T \phi (X) L \phi (X)^T \mathbf {v}). \end{aligned}$$

(5)

With the constraint $\hbox {Tr}(\mathbf {v}^T \phi (X) D \phi (X)^T \mathbf {v}) = 1$, minimizing the objective function in Eq. (5) is to solve the following generalized eigenvalue problem:

$$\begin{aligned} \phi (X) L \phi (X)^T \mathbf {v} = \lambda \phi (X) D \phi (X)^T \mathbf {v}. \end{aligned}$$

(6)

Note that each solution of problem (6) is a linear combination of $\psi _1, \ldots , \psi _N$, and there exits N-tuple $\mathbf {p} = (p_1, \ldots , p_N) \in \mathbb {R}^N$ such that $\mathbf {v} = \sum ^N_{i=1} p_i \psi _i = \phi (X) \mathbf {p}$. For matrix V consisting of all the solutions, there exists a matrix P such that $V= \phi (X)P$. Therefore, with the additional constraint $\hbox {Tr}(P^T \phi (X) D \phi (X)^T P)=1$, we can formulate the new objective function as follows:

$$\begin{aligned} \begin{aligned}&\mathop {\text {arg min}}\limits _{P, \alpha } \hbox {Tr}(P^T K L K P) \\&\text {s.t.}~ \hbox {Tr}(P^T K D K P)=1,~ \sum ^{M}_{i=1} \alpha _i=1,~ \alpha _i \ge 0, \end{aligned} \end{aligned}$$

(7)

or in the form without the trace constraint:

$$\begin{aligned} \begin{aligned}&\mathop {\text {arg min}}\limits _{P, \alpha } \frac{\hbox {Tr}(P^T K L K P)}{\hbox {Tr}(P^T K D K P)}, ~ \text {s.t.}~ \sum ^{M}_{i=1} \alpha _i=1,~ \alpha _i \ge 0. \end{aligned} \end{aligned}$$

(8)

Alternate Optimization via Relaxation

In this section, we employ a procedure of alternate optimization (Bezdek and Hathaway 2002; Tao et al. 2007) to derive the solution of the optimization problem. To the best of our knowledge, it is difficult to find its optimal solution directly, especially for the weights in (8). To optimize $\alpha$, we derive a relaxed objective function from the original problem. The output of the relaxed function can ensure that the value of the objective function in (8) is in a small neighborhood of the true minimum.

For a fixed $\alpha$, finding the optimal projection P is simply reduced to solve the generalized eigenvalue problem

$$\begin{aligned} KLK \mathbf {p} = \lambda KDK \mathbf {p}, \end{aligned}$$

(9)

and set $P = [\mathbf {p}_1, \ldots , \mathbf {p}_d]$ corresponds to the smallest d eigenvalues based on the Ky-Fan theorem (Bhatia 1997).

Next, we fix the projection P to update $\alpha$ individually. Without loss of generality, we first consider the condition that $M = 2$, i.e., there are only two views. Then the optimization problem (8) is reduced to

$$\begin{aligned} \begin{aligned}&\mathop {\text {arg min}}\limits _{P, \alpha } \frac{\hbox {Tr}(P^T K L K P)}{\hbox {Tr}(P^T K D K P)},~ \alpha _1 + \alpha _2 =1,~ \alpha _1, \alpha _2 \ge 0. \end{aligned} \end{aligned}$$

(10)

For simplicity, we denote $L_{ijk} = \hbox {Tr}(P^T K_i L_k K_j P)$ and $D_{ijk} = \hbox {Tr}(P^T K_i D_k K_j P)$, $i, j, k \in \{1, 2\}$. Then we can simply find that $L_{ijk} = L_{jik}$ and $D_{ijk} = D_{jik}$.

With the Cauchy-Schwarz inequality (Hardy et al. 1952), the relaxation for the objective function in (10) is shown in Eq. (11),

$$\begin{aligned} \frac{\hbox {Tr}(P^T K L K P)}{\hbox {Tr}(P^T K D K P)}= & {} \frac{\hbox {Tr}\Big (P^T (\alpha _1 K_1 + \alpha _2 K_2) (\alpha _1 L_1 + \alpha _2 L_2) (\alpha _1 K_1 + \alpha _2 K_2) P\Big )}{\hbox {Tr}\Big (P^T (\alpha _1 K_1 + \alpha _2 K_2) (\alpha _1 L_1 + \alpha _2 L_2) (\alpha _1 K_1 + \alpha _2 K_2) P\Big )} \nonumber \\= & {} \frac{\alpha _1^3 L_{111} + 2\alpha _1^2 \alpha _2 L_{121} + \alpha _1 \alpha _2^2 L_{221} + \alpha _1^2 \alpha _2 L_{112} + 2 \alpha _1 \alpha _2^2 L_{122} + \alpha _2^3 L_{222}}{\alpha _1^3 D_{111} + 2\alpha _1^2 \alpha _2 D_{121} + \alpha _1 \alpha _2^2 D_{221} + \alpha _1^2 \alpha _2 D_{112} + 2 \alpha _1 \alpha _2^2 D_{122} + \alpha _2^3 D_{222}}\nonumber \\\le & {} \frac{1}{\alpha _1^3 L_{111} + 2\alpha _1^2 \alpha _2 L_{121} + \alpha _1 \alpha _2^2 L_{221} + \alpha _1^2 \alpha _2 L_{112} + 2 \alpha _1 \alpha _2^2 L_{122} + \alpha _2^3 L_{222}} \nonumber \\&\times \left( \frac{\left( \alpha _1^3 L_{111}\right) ^2}{\alpha _1^3 D_{111}} + \frac{\left( 2\alpha _1^2 \alpha _2 L_{121}\right) ^2}{2\alpha _1^2 \alpha _2 D_{121}} + \frac{\left( \alpha _1 \alpha _2^2 L_{221}\right) ^2}{\alpha _1 \alpha _2^2 D_{221}} + \frac{\left( \alpha _1^2 \alpha _2 L_{112}\right) ^2}{\alpha _1^2 \alpha _2 D_{112}} + \frac{\left( 2 \alpha _1 \alpha _2^2 L_{122}\right) ^2}{2 \alpha _1 \alpha _2^2 D_{122}} + \frac{\left( \alpha _2^3 L_{222}\right) ^2}{\alpha _2^3 D_{222}}\right) \nonumber \\= & {} \frac{1}{\alpha _1^3 L_{111} + 2\alpha _1^2 \alpha _2 L_{121} + \alpha _1 \alpha _2^2 L_{221} + \alpha _1^2 \alpha _2 L_{112} + 2 \alpha _1 \alpha _2^2 L_{122} + \alpha _2^3 L_{222}} \nonumber \\&\times \left( \alpha _1^3 L_{111} \frac{L_{111}}{D_{111}} + 2\alpha _1^2 \alpha _2 L_{121} \frac{L_{121}}{D_{121}} + \alpha _1 \alpha _2^2 L_{221} \frac{L_{221}}{D_{221}} + \alpha _1^2 \alpha _2 L_{112} \frac{L_{112}}{D_{112}} + 2 \alpha _1 \alpha _2^2 L_{122} \frac{L_{122}}{D_{122}} + \alpha _2^3 L_{222} \frac{L_{222}}{D_{222}}\right) \nonumber \\= & {} \sum _{i,j,k \in \{1,2\}} w_{ijk}(\alpha _1, \alpha _2) \frac{L_{ijk}}{D_{ijk}}, \end{aligned}$$

(11)

where $w_{ijk}$ is the coefficient of $\frac{L_{ijk}}{D_{ijk}}$ and $\sum _{i,j,k \in \{1,2\}} w_{ijk} =1$. In this way, the objective function in (10) is relaxed to a weighted sum of $\frac{L_{ijk}}{D_{ijk}}$. Thus, minimizing the weighted sum of the right-hand-side in (11) can lower the objective function value in (10). Note that

$$\begin{aligned} \alpha _1^2 \alpha _1 = \frac{1}{2} \alpha _1 \cdot \alpha _1 \cdot 2\alpha _2 \le \frac{1}{2}\left( \frac{\alpha _1 + \alpha _1 + 2\alpha _2}{3}\right) ^3 = \frac{4}{27}, \end{aligned}$$

and then the weights without containing $\alpha _1^3$ and $\alpha _2^3$ are always smaller than a constant. Therefore, we only ensure that a part of the terms in the weighted sum is minimized, i.e., to solve the following optimization problem:

$$\begin{aligned} \mathop {\text {arg min}}\limits _{\alpha _1, \alpha _2} w_{111} \frac{L_{111}}{D_{111}} + w_{222} \frac{L_{222}}{D_{222}}. \end{aligned}$$

(12)

Since $w_{111}$ and $w_{222}$ are the functions of $(\alpha _1, \alpha _2)$, we first find the optimal weights without parameters. To avoid trivial solution, we assign an exponent $r > 1$ on each weight. The relaxed optimization will be

$$\begin{aligned} \mathop {\text {arg min}}\limits _{\beta _1, \beta _2} \beta _1^r \frac{L_{111}}{D_{111}} + \beta _2^r \frac{L_{222}}{D_{222}}, ~\text {s.t.}~ \beta _1 + \beta _2 =1, \beta _1, \beta _2 \ge 0. \end{aligned}$$

(13)

For (13), we have the Lagrangian function with the Lagrangian multiplier $\eta$:

$$\begin{aligned} L(\beta _1,\beta _2,\eta ) = \beta _1^r \frac{L_{111}}{D_{111}} + \beta _2^r \frac{L_{222}}{D_{222}} - \eta (\beta _1 + \beta _2 -1 ). \end{aligned}$$

(14)

We only need to set the derivatives of L with respect to $\beta _1$, $\beta _2$ and $\eta$ to zeros as follows:

$$\begin{aligned} \frac{\partial L}{\partial \beta _1}= & {} r \beta _1^{r-1} \frac{L_{111}}{D_{111}} - \eta =0, \end{aligned}$$

(15)

$$\begin{aligned} \frac{\partial L}{\partial \beta _2}= & {} r \beta _2^{r-1} \frac{L_{222}}{D_{222}} - \eta =0, \end{aligned}$$

(16)

$$\begin{aligned} \frac{\partial L}{\partial \eta }= & {} \beta _1 + \beta _2 -1 =0. \end{aligned}$$

(17)

Then $\beta _1$ and $\beta _2$ can be calculated by

$$\begin{aligned} \begin{aligned} \beta _1&= \frac{(L_{222} D_{111})^{\frac{1}{r-1}}}{(L_{222} D_{111})^{\frac{1}{r-1}} + (L_{111} D_{222})^{\frac{1}{r-1}}}, \\ \beta _2&= \frac{(L_{111} D_{222})^{\frac{1}{r-1}}}{(L_{222} D_{111})^{\frac{1}{r-1}} + (L_{111} D_{222})^{\frac{1}{r-1}}}. \end{aligned} \end{aligned}$$

(18)

Having acquired $\beta _1$ and $\beta _2$, we can obtain $\alpha _1$ and $\alpha _2$ by the corresponding relationship between the coefficients of the functions in (12) and (13):

$$\begin{aligned} \frac{\alpha _1^3 L_{111}}{\alpha _2^3 L_{222}} = \frac{w_{111}}{w_{222}} = \frac{\beta _1^r}{\beta _2^r}. \end{aligned}$$

(19)

With the constraint $\alpha _1 + \alpha _2 =1$, we can easily find that

$$\begin{aligned} \begin{aligned} \alpha _1&= \frac{\left( \beta _1^r L_{222}\right) ^{\frac{1}{3}}}{\left( \beta _1^r L_{222}\right) ^{\frac{1}{3}} + \left( \beta _2^r L_{111}\right) ^{\frac{1}{3}}}, \\ \alpha _2&= \frac{\left( \beta _2^r L_{111}\right) ^{\frac{1}{3}}}{\left( \beta _1^r L_{222}\right) ^{\frac{1}{3}} + \left( \beta _2^r L_{111}\right) ^{\frac{1}{3}}}. \end{aligned} \end{aligned}$$

(20)

Hence, for the general M-view situation, we also have the corresponding relaxed problems:

$$\begin{aligned} \mathop {\text {arg min}}\limits _{\sum ^M_{i=1} \alpha _i =1} \sum _{i,j,k \in \{1, \ldots , M\}} w_{ijk}(\alpha _1, \ldots , \alpha _M) \frac{L_{ijk}}{D_{ijk}} \end{aligned}$$

(21)

and

$$\begin{aligned} \mathop {\text {arg min}}\limits _{\beta _1, \ldots , \beta _M} \sum ^M_{i=1} \beta _i^r \frac{L_{iii}}{D_{iii}}, ~\text {s.t.}~ \sum ^M_{i=1} \beta _i = 1,~ \beta _i \ge 0. \end{aligned}$$

(22)

The coefficients $(\beta _1, \ldots , \beta _M)$ and $(\alpha _1, \ldots , \alpha _M)$ can be obtained in similar forms:

$$\begin{aligned} \beta _i = \frac{(D_{iii}/L_{iii})^{\frac{1}{r-1}}}{\sum ^M_{j=1}(D_{jjj}/L_{jjj})^{\frac{1}{r-1}}},~ i=1,\ldots , M \end{aligned}$$

(23)

and

$$\begin{aligned} \alpha _i = \frac{\left( \beta _i^r/L_{iii}\right) ^{\frac{1}{3}}}{\sum ^M_{j=1} \left( \beta _j^r/L_{jjj}\right) ^{\frac{1}{3}}},~ i=1,\ldots , M. \end{aligned}$$

(24)

Although the weight $\alpha$ obtained in the above procedure is not the global minimum, the objective function is ensured in a range of small values. We let $F_1$ and $F_2$ be the objective functions in (8) and (21), respectively, and let

$$\begin{aligned} F_3 = \sum _{i=j=k} w_{ijk} \frac{L_{ijk}}{D_{ijk}} = \sum ^M_{i=1} w_{iii} \frac{L_{iii}}{D_{iii}}. \end{aligned}$$

(25)

We can find that $F_1 \le F_2$ and if there exists $\alpha _i = 1$ for some i, then $F_1 = F_2 = F_3$. During the alternate procedure, for optimizing P, $F_1$ is minimized, and for optimizing $\alpha$, $F_3$ is minimized. Denote $m_1 = \max (F_1 - F_3)$ and $(P_1, \alpha _1) = \hbox {arg max} (F_1 - F_3)$, then we have

$$\begin{aligned} \begin{aligned} \min F_3 + m_1&\le F_3 (P_1, \alpha _1) + (F_1 - F_3)(P_1, \alpha _1) \\&= F_1 (P_1, \alpha _1) \le \max F_1, \end{aligned} \end{aligned}$$

and we can define the following nonnegative continuous function:

$$\begin{aligned} F_4 (P,\alpha ) = \max \Big (F_1(P, \alpha ), \min _{\alpha } \big (F_3(P, \alpha ) + m_1\big )\Big ). \end{aligned}$$

(26)

Note that $\min _{\alpha } \big (F_3(P, \alpha ) + m_1\big )$ is independent of $\alpha$, thus for any P, there exists $\alpha _0$, such that $F_1 (P, \alpha _0) = \min _{\alpha } \big (F_3(P, \alpha ) + m_1\big )$. If we impose the above alternate optimization on $F_4$, $F_4$ is nonincreasing and therefore converges. Though $\alpha$ dose not converge to a certain point, the range of $F_1$ is reduced to a small district, i.e., smaller than $\min _{\alpha } F_3$ plus a constant. It is also worthwhile to note that $F_3$ is actually the weighted sum of the objective functions for preserving each view’s locality information. However, the optimization for $F_3$ still learns information from each view separately, i.e., the locality similarity is not fused. We summarize the KMP in Algorithm 1.

During the testing phase, having acquired the data from each view $X_{test}^1, \cdots , X_{test}^M$ of a test video sequence $v_{test}$, we first compute the kernel values to form the representation of $v_{test}$ in RKHS of each view:

$$\begin{aligned} \mathbf {k}^i_{test} = (k_i(v_1, v_{test}), \cdots , k_i(v_N, v_{test})), ~i = 1, \ldots , M, \end{aligned}$$

where $k_i (\cdot , \cdot )$ is the kernel function defined in Sect.  3.3. Using the weights $(\alpha _1, \ldots , \alpha _M)$ optimized by Algorithm 1, we have the fused representation of $v_{test}$: $\mathbf {k}_{test} = \sum _{i=1}^M \alpha _i \mathbf {k}^i_{test}$. Then the final fused representation of $v_{test}$ in the reduced space is $\mathbf {y}_{test} = \mathbf {k}_{test} P$.

Experiments and Results

In this section, we evaluate our KMP systematically on five action datasets: KTH (Schuldt et al. 2004), UCF YouTube (Liu et al. 2009), UCF Sports (Rodriguez et al. 2008), Hollywood2 (Marszalek et al. 2009) and HMDB51 (Kuehne et al. 2011) respectively. Some representative frames of these datasets are illustrated in Fig. 4. In the rest of this section, we will first introduce the details of the used datasets and their corresponding experimental settings. After that, the compared results will be presented and discussed.

Datasets

The KTH dataset is the benchmark dataset commonly used for action recognition with 599 video clips. Particularly, it contains six different action classes (i.e., boxing, handclapping, handwaving, jogging, running and walking), which are performed by 25 subjects under 4 different scenarios. Following the pre-processing step mentioned in (Yao et al. 2010), the coarse 3D bounding boxes are extracted from all the raw action sequences and further normalized into an equal size of $100\times 100$ of each frame. In our experiments, we adopt two usually used settings to compare the final results. The first one is the original experimental setting of the authors, i.e., divide the data into a test set with nine subjects: 2, 3, 5, 6, 7, 8, 9, 10, 22 and the rest form the training set. We finally report the average accuracy over all classes as the performance measure. The other setting is the common leave-one-person-out cross-validation.

Fig. 4

Some example frames of five datasets: KTH, UCF YouTube, UCF Sports, Hollywood2 and HMDB51 (ordered from the top to the bottom)

The UCF YouTube dataset contains 1168 video clips with 11 action categories: basketball shooting, biking/cycling, diving, golf swinging, horse back riding, soccer juggling, swinging, tennis swinging, trampoline jumping, volleyball spiking, and walking with a dog. We also extract the bounding boxes according to the original paper (Liu et al. 2009). Each frame of the sequences is further normalized into the size of $100\times 100$. This dataset is relatively challenging due to large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, and illumination conditions. Following the original setup in (Liu et al. 2009), a leave-one-out scheme is adopted. The average accuracy over all classes is reported as the final performance.

The UCF Sports dataset has 10 classes of human actions with 150 collected broadcast videos. This collection represents a natural pool of actions featured in a wide range of scenes and viewpoints with a large intra-class variability. For this dataset, we use the provided bounding boxes and resize each video frame to a normalized size of $100\times 100$. In our experiments, we use a fivefold cross- validation setup mentioned in (Rodriguez et al. 2008), adopting 4/5th of the total number of sequences in each category for training and the rest for testing. The final recognition rate is averaged over the fivefolds.

The Hollywood2 dataset is a collection of 1707 action samples comprising 12 types of action from 69 different Hollywood movies. For this dataset, we deliberately use the full-sized sequences without any bounding boxes. In our experiments, we use the proposed KMP on a training set of 823 sequences and a test set with 884 sequences following the original setting.

The HMDB51 dataset contains 6849 realistic action sequences collected from a variety of movies and online videos. Specifically, it has 51 action classes and each has at least 101 positive samples. In our experiments, coarse bounding boxes have been extracted from all the sequences through masks released with the dataset and initialized into the size of $100\times 120$ for each frame. We adopt the official setting of (Kuehne et al. 2011) with three train/test splits. Each split has 70 training and 30 testing clips for each class.

Multiview Pose Feature Extraction

With the increasing complexity of recognition scenarios, using a single type of feature representation is difficult to satisfy the required accuracies in vision tasks, especially for some realistic applications.

Given a frame containing one pose, we would like to first describe it with multiview informative features. The descriptors are expected to capture the gradient, motion, texture and color information, which are the main cues of a pose. We, therefore, employ the HOF (Laptev et al. 2008), the histogram of oriented gradients (HOG) (Dalal and Triggs 2005), the local binary pattern (LBP) (Ahonen et al. 2004) and color histogram (ColorHist), respectively, for pose representation.

HOF: A fast and effective algorithm to capture the action movement based on the Lucas-Kanade optical flow. Specifically, we calculate HOF between any adjacent frames and each motion region is divided into sub-regions with a $5\times 5$ grid. For each sub-region, a 12-bin histogram is computed to accumulate the motion orientation within 360 degrees. Thus, the length of the final vector of HOF is $5\times 5\times 12=300$.

HOG: A powerful gradient descriptor. In particular, a 9-bin histogram over [0,180] degrees is computed to accumulate the gradient orientation over a $5\times 5$ cell. The length of the vector is $5\times 5\times 9=225$.

LBP: LBP features tolerate against illumination changes and are computationally efficient. The operator labels the pixels of an image by thresholding a $3\times 3$ neighborhood of each pixel with the center value and considering the results as a binary number and a 256-bin histogram of the LBP labels computed over a region is used as a texture descriptor.

Note that, all the above three features are extracted on the gray-scale frames.

ColorHist: For each channel of RGB, a 64-bin histogram is used. Thus the final ColorHist has $3\times 64=192$ dimensions.

In this way, each pose from a video frame is represented by four different feature views which can describe the thorough information of this frame/pose.

Table 1 Dimensions of four features for action recognition

Compared Methods and Settings

For action recognition, a video sequence can be usually described using differentfeature representations, i.e., multiview representation, in high dimensional feature spaces. In this paper, we adopt four different feature representations (i.e., HOG, HOF, LBP, ColorHist) to describe a video sequence. Table 1 illustrates the original dimensions of these features. We systematically compare our proposed KMP with two related multi-kernel fusion methods. In particular, KMP denotes that the RBF sequential kernels are combined by the proposed method:

$$\begin{aligned} K = \sum _{i=1}^M \alpha _i K_i, \end{aligned}$$

where the weight $\alpha _{i}$ is obtained via alternate optimization. AM indicates that the kernels are combined by arithmetic mean:

$$\begin{aligned} K_{AM} =\frac{1}{M}\sum _{i=1}^M K_i, \end{aligned}$$

and GM denotes the combination of kernels through geometric mean:

$$\begin{aligned} K_{GM} =\left( \prod _{i=1}^M K_i\right) ^{\frac{1}{M}}. \end{aligned}$$

Besides, we also include the best performance of the single-view-based spectral projection (BSP), the average performance of the single-view-based spectral projection (ASP) and concatenation of multiview embeddings in our compared experiments. All of AM, GM , BSP, ASP and multiview embedding concatenation are based on the locality preserving projections (LPP) (He and Niyogi 2004) technique. In addition, two non-linear embedding methods DSE and MSE are adopted in our comparison, as well. In DSE and MSE, the Laplacian embedding (LE) (Belkin and Niyogi 2001) is adopted.

All of the above methods are evaluated on seven different lengths of codes (20, 30, 40, 50, 60, 70, 80). Under the same experimental setting, all the parameters used in the compared methods have been strictly chosen according to their original papers. For KMP/MSE, the optimal balance parameter r for each dataset is selected from one of {2, 3, 4, 5, 6, 7, 8, 9, 10 } with the step of 1, which yields the best performance by ninefold cross-validation on the training data. The best $\sigma$ in kernel construction is also selected by the cross-validation on the training data. All experiments are performed using Matlab 2013a on a server configured with a 12-core processor and 128 G of RAM running the Linux OS (Table 2).

Table 2 Runtime(seconds) of the training and test phases with d = 80 on different datasets

Results

In Table 3, we first illustrate the performance of the single-view representation on all five datasets. In detail, we compute the RBF sequential kernel and weight matrix for a certain single view and input them to our KMP system. Since only a single view is used in KMP, it can be regarded as the procedure of kernelized LPP. From the comparison, we can easily observe that the HOG and HOF features consistently outperform the LBP descriptor in low dimensional feature space. The lowest accuracy is always obtained by ColorHist. Furthermore, we also include the long representation, which is concatenated by all the four low-dimensional feature representations, and the proposed KMP for multiview fusion based reduction into this comparison. It is shown that the concatenated representation can reach better performance than any of the single views, but is significantly lower than our KMP. Specifically, the best accuracies achieved by KMP are 97.5, 87.6, 95.8, 64.3 and 49.8 % on KTH, UCF YouTube, UCF Sport, Hollywood2 and HMDB51, respectively. Additionally, the results of the multiple kernel learning based on SVM (MKL-SVM) (Gönen and Alpaydin 2011) are listed in Table 3 using the same four feature descriptors. The training time and the test time of KMP are listed in Table 2. The runtime of the training phase includes the multiview feature extraction, the INBF process, the construction of kernel matrices via DTW and the optimization of KMP.

Fig. 5

Illustration of low-dimensional distributions of three different multi-kernel fusion schemes (illustrated with data of five actions from the HMDB51 dataset)

Table 3 Performance comparison (%) between the proposed KMP and single feature representations

In Tables 4, 5 and 6, six different multiview embedding schemes are compared with the proposed KMP on the KTH, UCF YouTube and UCF Sports respectively. From the whole tendency, the proposed KMP always leads to the best performance for action recognition. Meanwhile, arithmetic mean (AM) and geometric mean (GM) achieve higher recognition accuracies than the best performance of the single-view-BSP and the ASP. DSE produces worse performance than MSE and sometimes even obtains lower results than AM, but generates better performance than others, since a more meaningful multiview combination scheme is adopted in DSE. Beyond these, it is obviously observed that, with different target dimensions, the final results change a lot. Although both KMP and MSE consider the similarity matrix of each view, KMP maps data into the RKHS which is more suitable for linearly inseparable data in realistic situations. Usually, the best results via KMP appear from d = 50 to d = 80. For instance, the highest accuracy on the KTH dataset is on the dimension of 60 and the best performance on the UCF Sports and UCF YouTube happens when d = 50 and d = 80, respectively (Fig. 5).

Fig. 6

Performance comparison (%) on the Hollywood2 dataset with different feature fusion methods

Fig. 7

Performance comparison (%) on the HMDB51 dataset with different feature fusion methods

Table 4 Performance comparison (%) on the KTH dataset with different feature fusion methods

Table 5 Performance comparison (%) on the UCF YouTube dataset with different feature fusion methods

Table 6 Performance comparison (%) on the UCF Sports dataset with different feature fusion methods

Similar behaviors can also be seen on the Hollywood2 and HMDB51 datasets. From Fig. 6, we can observe that with the increase of the dimension, all the curves of compared methods on the Hollywood2 dataset are climbing up except for ASP and BSP, both of which have a decrease when the dimension exceeds 70. However, on the HMDB51 dataset, the results in comparison always climb up then go down when the length of dimension increases (see Fig. 7). Besides, from these figures, we can also discover that all the curves have the same tendency of change. All of the above compared methods including MKL-SVM are trained on the same multiview features after INBF.

Furthermore, Table 7 illustrates the performance variation of KMP with respect to the balance parameter r; the dimensionality of the low-dimensional embedding d is fixed at 20,30 and 40 respectively on the KTH dataset. By adopting the ninefold cross-validation scheme on the training data, it is demonstrated that the higher dimension prefers a larger r in our KMP. Moreover, Fig. 5 shows the low-dimensional (2-dimensional) embeddings obtained by AM, GM and KMP on the HMDB51 dataset. Our proposed KMP can well separate different categories, since it takes the semantically meaningful data structure of different views into consideration for embedding. The effectiveness of the INBF procedure in the training phase is demonstrated in Table 8.

At last, we also compare our results with the state-of-the-art approaches published in major vision conferences and journals in Table 9. In a sense, this kind of comparison is not fair enough, since different features with different methods are applied in different publications. Thus, we only treat this as a general evaluation of recent results. For the four datasets: KTH, UCF YouTube, UCF Sports and Hollywood2, our KMP approach either outperforms state-of-the-art methods or achieves the competitive results compared with published results. For the HMDB51 dataset, the proposed KMP has not shown better results than that reported in Wang and Schmid (2013) and Simonyan and Zisserman (2014) due to the powerful features they introduced, but doubles the result shown in the original paper that introduced this dataset (Kuehne et al. 2011). As a dimensionality reduction method, the proposed KMP can also adopt trajectory-based features or deep-learned features as different views for multiview learning. Considering that our action representation is semi-holistic and does not require an interest points detection phase, the results achieved by KMP are outstanding.

Table 7 Performance (%) of KMP with different r values on the KTH dataset

Table 8 The effectiveness (%) for INBF with $d=80$ on different datasets

Table 9 Performance comparison (%) of KMP with state-of-the-art methods in the literature

Conclusion

In this paper, we have presented an effective subspace learning framework based on KMP for action recognition. KMP can encode a variety of features in different ways, to achieve a semantically meaningful embedding. Specifically, KMP is able to successfully explore the complementary property of different views and finally finds a unique low-dimensional subspace where the distribution of each view is sufficiently smooth and discriminative. KMP can be regarded as a fused dimensionality reduction method for multiview data.

We have systematically evaluated our approach on five human action datasets: KTH, UCF YouTube, UCF Sports, Hollywood2 and HMDB51, and the corresponding results show that the proposed approach achieves better or competitive results with state-of-the-art methods. For future work, we plan to combine the current KMP approach with semi-supervised learning for other computer vision tasks.