Abstract
Learning short binary codes is challenged by the inherent discrete nature of the problem. The graph cuts algorithm is a well-studied discrete label assignment solution in computer vision, but has not yet been applied to solve the binary coding problems. This is partially because it was unclear how to use it to learn the encoding (hashing) functions for out-of-sample generalization. In this paper, we formulate supervised binary coding as a single optimization problem that involves both the encoding functions and the binary label assignment. Then we apply the graph cuts algorithm to address the discrete optimization problem involved, with no continuous relaxation. This method, named as Graph Cuts Coding (GCC), shows competitive results in various datasets.
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Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. In: SIGGRAPH (2004)
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: FOCS, pp. 459–468 (2006)
Blake, A., Kohli, P., Rother, C.: Markov random fields for vision and image processing. The MIT Press (2011)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. TPAMI (2004)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: ICCV (1999)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 273–297 (1995)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. JMLR, 1871–1874 (2008)
Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization for approximate nearest neighbor search. In: CVPR (2013)
Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization. TPAMI (2014)
Gong, Y., Lazebnik, S.: Iterative quantization: A procrustean approach to learning binary codes. In: CVPR (2011)
Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The Elements of Statistical Learning. Springer, New York (2009)
He, K., Sun, J.: Statistics of patch offsets for image completion. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 16–29. Springer, Heidelberg (2012)
He, K., Wen, F., Sun, J.: K-means Hashing: an Affinity-Preserving Quantization Method for Learning Binary Compact Codes. In: CVPR (2013)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC, pp. 604–613 (1998)
Kolmogorov, V., Rother, C.: Minimizing nonsubmodular functions with graph cuts-a review. TPAMI (2007)
Krizhevsky, A.: Cifar-10, http://www.cs.toronto.edu/~kriz/cifar.html
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks (2012)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS, pp. 1042–1050 (2009)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: ICCV (2009)
Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. In: SIGGRAPH, pp. 277–286 (2003)
Liu, W., Wang, J., Ji, R., Jiang, Y.-G., Chang, S.-F.: Supervised hashing with kernels. In: CVPR (2012)
Liu, W.: Large-Scale Machine Learning for Classification and Search. Ph.D. thesis, Columbia University (2012)
Liu, W., Wang, J., Kumar, S., Chang, S.-F.: Hashing with graphs. In: ICML (2011)
Norouzi, M., Fleet, D.: Cartesian k-means. In: CVPR (2013)
Norouzi, M.E., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: ICML, pp. 353–360 (2011)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV (2001)
Pritch, Y., Kav-Venaki, E., Peleg, S.: Shift-map image editing. In: ICCV (2009)
Rastegari, M., Farhadi, A., Forsyth, D.: Attribute discovery via predictable discriminative binary codes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 876–889. Springer, Heidelberg (2012)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 7–42 (2002)
Shen, F., Shen, C., Shi, Q., van den Hengel, A., Tang, Z.: Inductive hashing on manifolds. In: CVPR (2013)
Tan, R.T.: Visibility in bad weather from a single image. In: CVPR, pp. 1–8 (2008)
Torralba, A.B., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: CVPR (2008)
Vedaldi, A., Fulkerson, B.: Vlfeat: An open and portable library of computer vision algorithms. In: Proceedings of the International Conference on Multimedia, pp. 1469–1472. ACM (2010)
Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. TPAMI, 480–492 (2012)
Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for scalable image retrieval. In: CVPR (2010)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760 (2008)
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Ge, T., He, K., Sun, J. (2014). Graph Cuts for Supervised Binary Coding. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8695. Springer, Cham. https://doi.org/10.1007/978-3-319-10584-0_17
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