Abstract
This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets.
Chapter PDF
Similar content being viewed by others
References
Bell, A.J., Sejnowski, T.J.: The “independent components” of natural scenes are edge filters. Vision research 37(23), 3327–3338 (1997)
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)
Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV (2005)
Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: Wld: A robust local image descriptor. TPAMI 32(9), 1705–1720 (2010)
Chen, L.C., Papandreou, G., Yuille, A.L.: Learning a dictionary of shape epitomes with applications to image labeling: Supplementary material (2013)
Coates, A., Ng, A.Y., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: ICAIS (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Dana, K.J., Van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real-world surfaces. TOG 18(1), 1–34 (1999)
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH (2001)
Epshtein, B., Uliman, S.: Feature hierarchies for object classification. In: ICCV (2005)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88(2), 303–338 (2010)
Guo, Y., Zhao, G., Pietikäinen, M.: Texture classification using a linear configuration model based descriptor. In: BMVC (2011)
Guo, Y., Zhao, G., Pietikäinen, M.: Discriminative features for texture description. PR 45(10), 3834–3843 (2012)
Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)
Jojic, N., Frey, B.J., Kannan, A.: Epitomic analysis of appearance and shape. In: CVPR (2003)
Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV 43(1), 29–44 (2001)
Liang, L., Liu, C., Xu, Y.Q., Guo, B., Shum, H.Y.: Real-time texture synthesis by patch-based sampling. TOG 20(3), 127–150 (2001)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR (2008)
Mao, J., Li, H., Zhou, W., Yan, S., Tian, Q.: Scale based region growing for scene text detection. ACM Multimedia, 1007–1016 (2013)
Matthews, T., Nixon, M.S., Niranjan, M.: Enriching texture analysis with semantic data. In: CVPR (2013)
Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllonen, J., Huovinen, S.: Outex-new framework for empirical evaluation of texture analysis algorithms. In: ICPR (2002)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7), 971–987 (2002)
Papandreou, G., Chen, L.C., Yuille, A.L.: Modeling image patches with a generic dictionary of mini-epitomes. In: CVPR (2014)
Pele, O., Werman, M.: The quadratic-chi histogram distance family. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 749–762. Springer, Heidelberg (2010)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)
Sharma, G., ul Hussain, S., Jurie, F.: Local higher-order statistics (lhs) for texture categorization and facial analysis. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 1–12. Springer, Heidelberg (2012)
Sifre, L., Mallat, S., DI, E.N.S.: Rotation, scaling and deformation invariant scattering for texture discrimination. In: CVPR (2013)
Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 73–86. Springer, Heidelberg (2012)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. TIP 19(6), 1635–1650 (2010)
Ullman, S., Sali, E.: Object classification using a fragment-based representation. In: BMCV (2000)
Valkealahti, K., Oja, E.: Reduced multidimensional co-occurrence histograms in texture classification. TPAMI 20(1), 90–94 (1998)
Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. TPAMI 31(11), 2032–2047 (2009)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1800–1807. IEEE (2005)
Wolf, L., Huang, X., Martin, I., Metaxas, D.: Patch-based texture edges and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 481–493. Springer, Heidelberg (2006)
Wu, Y.N., Si, Z., Gong, H., Zhu, S.C.: Learning active basis model for object detection and recognition. IJCV 90(2), 198–235 (2010)
Ye, X., Yuille, A.: Learning a dictionary of deformable patches using gpus. In: Workshop on GPU’s in Computer Vision Applications, ICCV (2011)
Zhu, S.C., Wu, Y., Mumford, D.: Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling. IJCV 27(2), 107–126 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mao, J., Zhu, J., Yuille, A.L. (2014). An Active Patch Model for Real World Texture and Appearance Classification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, Cham. https://doi.org/10.1007/978-3-319-10578-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-10578-9_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10577-2
Online ISBN: 978-3-319-10578-9
eBook Packages: Computer ScienceComputer Science (R0)