Sparse Representations for Efficient Shape Matching
作者： A Noma , R M Cesar
关键词:
摘要: Graph matching is a fundamental problem with many applications in computer vision. Patterns are represented by graphs and pattern recognition corresponds to finding correspondence between vertices from different graphs. In cases, the can be formulated as quadratic assignment problem, where cost function consists of two components: linear term representing vertex compatibility encoding edge compatibility. The NP-hard present paper extends approximation technique based on graph efficient belief propagation, described previous work, using sparse representations for shape matching. Successful results 3D objects handwritten digits illustrated, COIL MNIST datasets, respectively.
sci-hub.se 参考文章(16)
Luiz Velho, Roberto M. Cesar, A Computer-Assisted Colorization Approach Based on Efficient Belief Propagation and Graph Matching iberoamerican congress on pattern recognition. ,vol. 5856, pp. 345- 352 ,(2009) , 10.1007/978-3-642-10268-4_41
Lorenzo Torresani, Vladimir Kolmogorov, Carsten Rother, Feature Correspondence Via Graph Matching: Models and Global Optimization Lecture Notes in Computer Science. pp. 596- 609 ,(2008) , 10.1007/978-3-540-88688-4_44
S. Belongie, J. Malik, J. Puzicha, Shape matching and object recognition using shape contexts IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 24, pp. 509- 522 ,(2002) , 10.1109/34.993558
Y. Weiss, W.T. Freeman, On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs IEEE Transactions on Information Theory. ,vol. 47, pp. 736- 744 ,(2001) , 10.1109/18.910585
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition Proceedings of the IEEE. ,vol. 86, pp. 2278- 2324 ,(1998) , 10.1109/5.726791
Terry Caelli, Tiberio Caetano, Graphical models for graph matching: Approximate models and optimal algorithms Pattern Recognition Letters. ,vol. 26, pp. 339- 346 ,(2005) , 10.1016/J.PATREC.2004.10.022
Hiroshi Murase, Shree K. Nayar, Visual learning and recognition of 3-D objects from appearance International Journal of Computer Vision. ,vol. 14, pp. 5- 24 ,(1995) , 10.1007/BF01421486
M. van Eede, D. Macrini, A. Telea, C. Sminchisescu, S.S. Dickinson, Canonical Skeletons for Shape Matching international conference on pattern recognition. ,vol. 2, pp. 64- 69 ,(2006) , 10.1109/ICPR.2006.354
T.S. Caetano, J.J. McAuley, Li Cheng, Q.V. Le, A.J. Smola, Learning Graph Matching IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 31, pp. 1048- 1058 ,(2009) , 10.1109/TPAMI.2009.28
Longbin Chen, Julian J. McAuley, Rogerio S. Feris, Tiberio S. Caetano, Matthew Turk, Shape classification through structured learning of matching measures computer vision and pattern recognition. pp. 365- 372 ,(2009) , 10.1109/CVPR.2009.5206792