nips nips2012 nips2012-146 nips2012-146-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Tatsuya Harada, Yasuo Kuniyoshi
Abstract: This paper proposes a novel image representation called a Graphical Gaussian Vector (GGV), which is a counterpart of the codebook and local feature matching approaches. We model the distribution of local features as a Gaussian Markov Random Field (GMRF) which can efficiently represent the spatial relationship among local features. Using concepts of information geometry, proper parameters and a metric from the GMRF can be obtained. Then we define a new image feature by embedding the proper metric into the parameters, which can be directly applied to scalable linear classifiers. We show that the GGV obtains better performance over the state-of-the-art methods in the standard object recognition datasets and comparable performance in the scene dataset. 1
[1] S. Amari and H. Nagaoka. Methods of Information Geometry, volume 191 of Translations of mathematical monographs. American Mathematical Society, 2001.
[2] A.C. Berg, T.L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondence. In CVPR, 2005.
[3] L. Bo, X. Ren, and D. Fox. Kernel descriptors for visual recognition. In NIPS, 2010.
[4] O. Boiman, E. Shechtman, and M. Irani. In defense of nearest-neighbor based image classification. In CVPR, 2008.
[5] Y. Cao, C. Wang, Z. Li, L. Zhang, and L. Zhang. Spatial-bag-of-features. In CVPR, 2010.
[6] K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. Online passive-aggressive algorithms. JMLR, 7:551–585, 2006.
[7] G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In ECCV International Workshop on SLCV, 2004.
[8] O. Duchenne, A. Joulin, and J. Ponce. A graph-matching kernel for object categorization. In ICCV, 2011.
[9] J.D.R. Farquhar, S. Szedmak, H. Meng, and J. Shawe-Taylor. Improving “bag-of-keypoints” image categorisation: Generative models and pdf-kernels. Technical report, University of Southampton, 2005.
[10] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In CVPR, Workshop on GMBV, 2004.
[11] R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In CVPR, 2003.
[12] R. Fergus, P. Zisserman, and A. Perona. Weakly supervised scale-invariant learning of models for visual recognition. IJCV, 71(3):273–303, 2007.
[13] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007.
[14] T. Harada, H. Nakayama, and Y. Kuniyoshi. Improving local descriptors by embedding global and local spatial information. In ECCV, 2010.
[15] Jason K. Johnson. Convex Relaxation Methods for Graphical Models: Lagrangian and Maximum Entropy Approaches. PhD thesis, MIT, 2008.
[16] J. Kim and K. Grauman. Asymmetric region-to-image matching for comparing images with generic object categories. In CVPR, 2010.
[17] J. Krapac, J. Verbeek, and F. Jurie. Modeling spatial layout with fisher vectors for image categorization. In ICCV, 2011.
[18] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
[19] H. Nakayama, T. Harada, and Y. Kuniyoshi. Dense sampling low-level statistics of local features. In CIVR , 2009.
[20] H. Nakayama, T. Harada, and Y. Kuniyoshi. Global gaussian approach for scene categorization using information geometry. In CVPR, 2010.
[21] N. Otsu and T. Kurita. A new scheme for practical, flexible and intelligent vision systems. In Proc. IAPR Workshop on Computer Vision, 1988.
[22] F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007.
[23] F. Perronnin, C. Dance, G. Csurka, and M. Bressan. Adapted vocabularies for generic visual categorization. In ECCV, 2006.
[24] F. Perronnin, J. S´ nchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. a In ECCV, 2010.
[25] J. S´ nchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. a In CVPR, 2011.
[26] C. Wallraven, B. Caputo, and A. Graf. Recognition with local features: the kernel recipe. In ICCV, 2003.
[27] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained linear coding for image classification. In CVPR , 2010.
[28] J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.
[29] X. Zhou, K. Yu, T. Zhang, and T. S. Huang. Image classification using super-vector coding of local image descriptors. In ECCV, 2010. 9