The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - June (2011 vol.33)
pp: 1266-1273
Qingshan Liu , Rutgers University at New Brunswick, Piscataway
Fengjun Lv , NEC Laboratories America, Inc, Cupertino
Yihong Gong , NEC Laboratories America, Inc., Cupertino
Dimitris N. Metaxas , Rutgers University at New Brunswick, Piscataway
ABSTRACT
We present a framework for unsupervised image categorization in which images containing specific objects are taken as vertices in a hypergraph and the task of image clustering is formulated as the problem of hypergraph partition. First, a novel method is proposed to select the region of interest (ROI) of each image, and then hyperedges are constructed based on shape and appearance features extracted from the ROIs. Each vertex (image) and its k-nearest neighbors (based on shape or appearance descriptors) form two kinds of hyperedges. The weight of a hyperedge is computed as the sum of the pairwise affinities within the hyperedge. Through all of the hyperedges, not only the local grouping relationships among the images are described, but also the merits of the shape and appearance characteristics are integrated together to enhance the clustering performance. Finally, a generalized spectral clustering technique is used to solve the hypergraph partition problem. We compare the proposed method to several methods and its effectiveness is demonstrated by extensive experiments on three image databases.
INDEX TERMS
Unsupervised image categorization, hypergraph, hypergraph partition.
CITATION
Qingshan Liu, Fengjun Lv, Yihong Gong, Dimitris N. Metaxas, "Unsupervised Image Categorization by Hypergraph Partition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 6, pp. 1266-1273, June 2011, doi:10.1109/TPAMI.2011.25
REFERENCES
[1] S. Agarwal, K. Branson, and S. Belongie, “Higher Order Learning with Graphs,” Proc. Int'l Conf. Machine Learning, 2006.
[2] S. Agarwal, J. Lim, L. Zelnik Manor, P. Perona, D. Kriegman, and S. Belongie, “Beyond Pairwise Clustering,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[3] C.J. Alpert and A.B. Kahng, “Recent Directions in Netlist Partitioning: A Survey,” Integration: The Very Large Scale Integration J., vol. 19, pp. 1-81, 1995.
[4] E. Bart, I. Porteous, P. Perona, and M. Welling, “Unsupervised Learning of Visual Taxonomies,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[5] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded Up Robust Features,” Proc. European Conf. Computer Vision, 2006.
[6] J. Besag, “On the Statistical Analysis of Dirty Pictures,” J. Royal Statistical Soc., vol. B-48, no. 3, pp. 259-302, 1986.
[7] C.M. Bishop, Pattern Recognition and Machine Learning, Aug. 2006.
[8] M. Bolla, “Spectra, Euclidean Representations and Clustering of Hypergraphs,” Proc. Discrete Math., 1993.
[9] A. Bosch, A. Zisserman, and X. Munoz, “Representing Shape with a Spatial Pyramid Kernel,” Proc. Int'l Conf. Image and Video Retrieval, 2007.
[10] A. Bosch, A. Zisserman, and X. Munoz, “Image Classification Using Random Forests and Ferns,” Proc. IEEE Int'l Conf. Computer Vision, 2007.
[11] O. Chum and A. Zisserman, “An Exemplar Model for Learning Object Classes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[12] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results,” Proc. Conf. Advances in Neural Information Processing Systems, 2008.
[13] R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, “Learning Object Categories from Google's Image Search,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[14] B.J.J. Frey and D. Dueck, “Clustering by Passing Messages between Data Points,” Science, vol. 315, pp. 972-976, 2007.
[15] M. Fritz and B. Schiele, “Towards Unsupervised Discovery of Visual Categories,” Proc. 28th Ann. Symp. German Assoc. for Pattern Recognition, 2006.
[16] K. Grauman and T. Darrell, “The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[17] K. Grauman and T. Darrell, “Unsupervised Learning of Categories from Sets of Partially Matching Image Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[18] G. Griffin, A. Holub, and P. Perona, “Caltech-256 Object Category Dataset,” technical report, California Inst. of Tech nology, 2007.
[19] G. Griffin and P. Perona, “Learning and Using Taxonomies for Fast Visual Categorization,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[20] L. Karlinsky, M. Dinerstein, D. Levi, and S. Ullman, “Unsupervised Classification and Part Localization by Consistency Amplification,” Proc. European Conf. Computer Vision, 2008.
[21] G. Kim, C. Faloutsos, and M. Hebert, “Unsupervised Modeling of Object Categories Using Link Analysis Techniques,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[22] G. Kim and A. Torralba, “Unsupervised Detection of Regions of Interest Using Iterative Link Analysis,” Proc. Conf. Advances in Neural Information Processing Systems, 2009.
[23] C.H. Lampert, M.B. Blaschko, and T. Hofmann, “Beyond Sliding Windows: Object Localization by Efficient Subwindow Search,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[24] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[25] Y.J. Lee and K. Grauman, “Shape Discovery from Unlabeled Image Collections,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[26] F. Li, R. Fergus, and P. Perona, “Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories,” Proc. Conf. Computer Vision and Image Understanding, p. 106, 2007.
[27] F.-F. Li and P. Perona, “A Bayesian Hierarchical Model for Learning Natural Scene Categories,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[28] D. Liu and T. Chen, “Unsupervised Image Categorization and Object Localization Using Topic Models and Correspondences between Images,” Proc. IEEE Int'l Conf. Computer Vision, 2007.
[29] J.B. Macqueen, “Some Methods of Classification and Analysis of Multivariate Observations,” Proc. Fifth Berkeley Symp. Math. Statistics and Probability, pp. 281-297, 1967.
[30] T. Malisiewicz and A.A. Efros, “Beyond Categories: The Visual Memex Model for Reasoning about Object Relationships,” Proc. Conf. Advances in Neural Information Processing Systems, 2009.
[31] A.Y. Ng, M.I. Jordan, and Y. Weiss, “On Spectral Clustering: Analysis and an Algorithm,” Proc. Conf. Advances in Neural Information Processing Systems, 2001.
[32] J. Rodréquez, “On the Laplacian Spectrum and Walk-Regular Hypergraphs,” Linear and Multilinear Algebra, vol. 51, pp. 285-297, 2003.
[33] B.C. Russell, A.A. Efros, J. Sivic, W.T. Freeman, and A. Zisserman, “Using Multiple Segmentations to Discover Objects and Their Extent in Image Collections,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[34] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[35] J. Sivic, B.C. Russell, A.A. Efros, A. Zisserman, and W.T. Freeman, “Discovering Objects and Their Location in Images,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[36] J. Sivic, B.C. Russell, A. Zisserman, I. Ecole, and N. Suprieure, “Unsupervised Discovery of Visual Object Class Hierarchies,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[37] L. Sun, S. Ji, and J. Ye, “Hypergraph Spectral Learning for Multi-Label Classification,” Proc. ACM SIGKDD '08, 2008.
[38] T. Tuytelaars, C.H. Lampert, M.B. Blaschko, and W. Buntine, “Unsupervised Object Discovery: A Comparison,” Proc. Int'l J. Computer Vision, 2009.
[39] J. van Gemert, J. Geusebroek, C. Veenman, and A. Smeulders, “Kernel Codebooks for Scene Categorization,” Proc. European Conf. Computer Vision, 2008.
[40] R. Zass and A. Shashua, “Probabilistic Graph and Hypergraph Matching,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[41] D. Zhou, J. Huang, and B. Schökopf, “Learning with Hypergraphs: Clustering, Classification, and Embedding,” Proc. Conf. Advances in Neural Information Processing Systems, 2007.
[42] J.Y. Zien, M.D.F. Schlag, and P.K. Chan, “Multi-Level Spectral Hypergraph Partitioning with Arbitrary Vertex Sizes,” Proc. IEEE Int'l Conf. Computer-Aided Design, pp. 201-204, 1996.
38 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool