Image Categorization Using Directed Graphs

Most existing graph-based semi-supervised classification methods use pairwise similarities as edge weights of an undirected graph with images as the nodes of the graph. Recently several new graph construction methods produce, however, directed graph (asymmetric similarity between nodes). A simple symmetrization is often used to convert a directed graph to an undirected one. This, however, loses important structural information conveyed by asymmetric similarities. In this paper, we propose a novel symmetric co-linkage similarity which captures the essential relationship among the nodes in the directed graph.We apply this new co-linkage similarity in two important computer vision tasks for image categorization: object recognition and image annotation. Extensive empirical studies demonstrate the effectiveness of our method.

[1]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  M. KleinbergJon.  Authoritative sources in a hyperlinked environment , 1999 .

[3]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[4]  Dan Roth,et al.  Learning a Sparse Representation for Object Detection , 2002, ECCV.

[5]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[6]  Rajesh P. N. Rao,et al.  Probabilistic Models of the Brain: Perception and Neural Function , 2002 .

[7]  Chris H. Q. Ding,et al.  PageRank, HITS and a unified framework for link analysis , 2002, SIGIR '02.

[8]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[9]  Bernt Schiele,et al.  Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[11]  Bernt Schiele,et al.  Analyzing contour and appearance based methods for object categorization , 2003, CVPR 2003.

[12]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[13]  Chris H. Q. Ding,et al.  Link Analysis: Hubs and Authorities on the World Wide Web , 2004, SIAM Rev..

[14]  Yiming Yang,et al.  RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Chris H. Q. Ding,et al.  A learning framework using Green's function and kernel regularization with application to recommender system , 2007, KDD '07.

[17]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[18]  Wei Liang,et al.  A Graph Based Subspace Semi-supervised Learning Framework for Dimensionality Reduction , 2008, ECCV.

[19]  Fei Wang,et al.  Label Propagation through Linear Neighborhoods , 2008, IEEE Trans. Knowl. Data Eng..

[20]  Shuicheng Yan,et al.  Semi-supervised Learning by Sparse Representation , 2009, SDM.

[21]  Hong Cheng,et al.  Sparsity induced similarity measure for label propagation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Chris H. Q. Ding,et al.  Image annotation using multi-label correlated Green's function , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[24]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.