A Dual Decomposition Approach to Feature Correspondence
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[1] Vladimir Kolmogorov,et al. Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Martin J. Wainwright,et al. MAP estimation via agreement on trees: message-passing and linear programming , 2005, IEEE Transactions on Information Theory.
[3] Daniel Tarlow,et al. Using Combinatorial Optimization within Max-Product Belief Propagation , 2006, NIPS.
[4] Lixin Fan,et al. Categorizing Nine Visual Classes using Local Appearance Descriptors , 2004 .
[5] Vladimir Kolmogorov,et al. Computing visual correspondence with occlusions using graph cuts , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[6] Jitendra Malik,et al. Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[7] Christoph Schnörr,et al. Probabilistic Subgraph Matching Based on Convex Relaxation , 2005, EMMCVPR.
[8] P. Torr. Geometric motion segmentation and model selection , 1998, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[9] Endre Boros,et al. Pseudo-Boolean optimization , 2002, Discret. Appl. Math..
[10] Nikos Komodakis,et al. MRF Optimization via Dual Decomposition: Message-Passing Revisited , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[11] Barbara Caputo,et al. Recognition with local features: the kernel recipe , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[12] Peter N. Belhumeur,et al. A binocular stereo algorithm for reconstructing sloping, creased, and broken surfaces in the presence of half-occlusion , 1993, 1993 (4th) International Conference on Computer Vision.
[13] Vladimir Kolmogorov,et al. Optimizing Binary MRFs via Extended Roof Duality , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[14] Alexei A. Efros,et al. Discovering object categories in image collections , 2005 .
[15] Gabriela Csurka,et al. Visual categorization with bags of keypoints , 2002, eccv 2004.
[16] Trevor Darrell,et al. The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[17] Norman I. Badler,et al. Multi-Level Shape Representation Using Global Deformations and Locally Adaptive Finite Elements , 1997, International Journal of Computer Vision.
[18] Geir Dahl,et al. Lagrangian-based methods for finding MAP solutions for MRF models , 2000, IEEE Trans. Image Process..
[19] Alexei A. Efros,et al. Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[20] Steven Gold,et al. A Graduated Assignment Algorithm for Graph Matching , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[21] Endre Boros,et al. Preprocessing of unconstrained quadratic binary optimization , 2006 .
[22] C. Karen Liu,et al. Learning physics-based motion style with nonlinear inverse optimization , 2005, ACM Trans. Graph..
[23] Jianbo Shi,et al. Balanced Graph Matching , 2006, NIPS.
[24] Cordelia Schmid,et al. Selection of scale-invariant parts for object class recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[25] Pierre Hansen,et al. Roof duality, complementation and persistency in quadratic 0–1 optimization , 1984, Math. Program..
[26] Hanif D. Sherali,et al. Convergence and Computational Analyses for Some Variable Target Value and Subgradient Deflection Methods , 2006, Comput. Optim. Appl..
[27] P. Chardaire,et al. A Decomposition Method for Quadratic Zero-One Programming , 1995 .
[28] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[29] Martial Hebert,et al. An Integer Projected Fixed Point Method for Graph Matching and MAP Inference , 2009, NIPS.
[30] Alex Pentland,et al. Modal Matching for Correspondence and Recognition , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[31] João Paulo Costeira,et al. A Global Solution to Sparse Correspondence Problems , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[32] Jitendra Malik,et al. Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.
[33] A. Victor Cabot,et al. Solving Certain Nonconvex Quadratic Minimization Problems by Ranking the Extreme Points , 1970, Oper. Res..
[34] Vladimir Kolmogorov,et al. Minimizing Nonsubmodular Functions with Graph Cuts-A Review , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Andrew Blake,et al. LogCut - Efficient Graph Cut Optimization for Markov Random Fields , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[36] Martial Hebert,et al. A spectral technique for correspondence problems using pairwise constraints , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[37] Alexander J. Smola,et al. Learning Graph Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Mario Vento,et al. Thirty Years Of Graph Matching In Pattern Recognition , 2004, Int. J. Pattern Recognit. Artif. Intell..
[39] Philip H. S. Torr,et al. Solving Markov Random Fields using Semi Definite Programming , 2003, AISTATS.
[40] Ian McGraw,et al. Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing , 2006, UAI.
[41] S. Lazebnik,et al. Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study , 2005 .
[42] Ravindra K. Ahuja,et al. Network Flows: Theory, Algorithms, and Applications , 1993 .
[43] Julian Yarkony,et al. Covering trees and lower-bounds on quadratic assignment , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[44] Tomás Werner,et al. A Linear Programming Approach to Max-Sum Problem: A Review , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.