Abstract
Image analysis tasks such as classification, clustering, detection, and retrieval are only as good as the feature representation of the images they use. Much research in computer vision is focused on finding better or semantically richer image representations. Bag of visual Words (BoW) is a representation that has emerged as an effective one for a variety of computer vision tasks. BoW methods traditionally use low level features. We have devised a strategy to use these low level features to create ‘‘higher level’’ features by making use of the spatial context in images. In this paper, we propose a novel hierarchical feature learning framework that uses a Naive Bayes Clustering algorithm to convert a 2-D symbolic image at one level to a 2-D symbolic image at the next level with richer features. On two popular datasets, Pascal VOC 2007 and Caltech 101, we empirically show that classification accuracy obtained from the hierarchical features computed using our approach is significantly higher than the traditional SIFT based BoW representation of images even though our image representations are more compact.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV (2003)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV (2004)
Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. In: CVPR (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)
van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel Codebooks for Scene Categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 696–709. Springer, Heidelberg (2008)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR (2010)
Zhou, X., Yu, K., Zhang, T., Huang, T.S.: Image Classification Using Super-Vector Coding of Local Image Descriptors. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 141–154. Springer, Heidelberg (2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)
Hinton, G.E., Osindero, S., Whye Teh, Y.: A fast learning algorithm for deep belief nets. In: Neural Computation (2006)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE, 2278–2324 (1998)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML (2009)
Riesenhuber, M., Poggio, T., Studies, E.: Hierarchical models of object recognition in cortex (1999)
Fergus, R., Yu, K., Ranzato, M.A., Lee, H., Salakhutdinov, R., Taylor, G.: Tutorial on deep learning methods for vision. In: CVPR 2012 Tutorial (2012), http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/
Agarwal, A., Triggs, B.: Multilevel image coding with hyperfeatures. International Journal of Computer Vision (2008)
Quack, T., Ferrari, V., Leibe, B., Gool, L.V.: Efficient mining of frequent and distinctive feature configurations (2007)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: WGMBV (2004)
Lazebnik, S., Schmid, C., Ponce, J.: A maximum entropy framework for part-based texture and object recognition. In: ICCV (2005)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: ICCV (2005)
Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: ICCV (2005)
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR (2009)
Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: BMVC (2011)
lan Boreau, Y., Bach, F., Lecun, Y., Ponce, J.: Learning mid-level features for recognition (2010)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics (2007)
Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: SODA (2007)
Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chandra, S., Kumar, S., Jawahar, C.V. (2013). Learning Hierarchical Bag of Words Using Naive Bayes Clustering. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-37331-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37330-5
Online ISBN: 978-3-642-37331-2
eBook Packages: Computer ScienceComputer Science (R0)