Graph cuts for supervised binary coding

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8695 LNCS, Issue PART 7, 2014, Pages 250-264

  Ge, Tiezheng ^a   He, Kaiming ^b   Sun, Jian ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ENCODING (SYMBOLS); GRAPHIC METHODS; OPTIMIZATION;

ASSIGNMENT SOLUTIONS; BINARY CODING; BINARY LABELS; CONTINUOUS RELAXATION; DISCRETE OPTIMIZATION PROBLEMS; ENCODING FUNCTIONS; GRAPH CUT; OPTIMIZATION PROBLEMS;

BINARY CODES;

EID: 84906335982     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10584-0_17     Document Type: Conference Paper

References (36)

1. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. In: SIGGRAPH (2004)
2. Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: FOCS, pp. 459-468 (2006)
3. Blake, A., Kohli, P., Rother, C.: Markov random fields for vision and image processing. The MIT Press (2011)
4. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. TPAMI (2004)
5. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: ICCV (1999)
6. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning, 273-297 (1995)
7. 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)
8. Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization for approximate nearest neighbor search. In: CVPR (2013)
9. Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization. TPAMI (2014)
10. Gong, Y., Lazebnik, S.: Iterative quantization: A procrustean approach to learning binary codes. In: CVPR (2011)
11. Hastie, T.J., Tibshirani, R.J., Friedman, J.H.: The Elements of Statistical Learning. Springer, New York (2009)
12. 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)
13. He, K., Wen, F., Sun, J.: K-means Hashing: an Affinity-Preserving Quantization Method for Learning Binary Compact Codes. In: CVPR (2013)
14. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC, pp. 604-613 (1998)
15. Kolmogorov, V., Rother, C.: Minimizing nonsubmodular functions with graph cutsa review. TPAMI (2007)
16. Krizhevsky, A.: Cifar-10, http://www.cs.toronto.edu/~kriz/cifar.html
17. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks (2012)
18. Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS, pp. 1042-1050 (2009)
19. Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: ICCV (2009)
20. 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)
21. Liu, W., Wang, J., Ji, R., Jiang, Y.-G., Chang, S.-F.: Supervised hashing with kernels. In: CVPR (2012)
22. Liu, W.: Large-Scale Machine Learning for Classification and Search. Ph.D. thesis, Columbia University (2012)
23. Liu, W., Wang, J., Kumar, S., Chang, S.-F.: Hashing with graphs. In: ICML (2011)
24. Norouzi, M., Fleet, D.: Cartesian k-means. In: CVPR (2013)
25. Norouzi, M.E., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: ICML, pp. 353-360 (2011)
26. Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV (2001)
27. Pritch, Y., Kav-Venaki, E., Peleg, S.: Shift-map image editing. In: ICCV (2009)
28. 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)
29. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 7-42 (2002)
30. Shen, F., Shen, C., Shi, Q., van den Hengel, A., Tang, Z.: Inductive hashing on manifolds. In: CVPR (2013)
31. Tan, R.T.: Visibility in bad weather from a single image. In: CVPR, pp. 1-8 (2008)
32. Torralba, A.B., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: CVPR (2008)
33. 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)
34. Vedaldi, A., Zisserman, A.: Efficient additive kernels via explicit feature maps. TPAMI, 480-492 (2012)
35. Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for scalable image retrieval. In: CVPR (2010)
36. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753-1760 (2008)