Learning compact binary descriptors with unsupervised deep neural networks

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 1183-1192

  Lin, Kevin ^a   Lu, Jiwen ^b   Chen, Chu Song ^a   Zhou, Jie ^b  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; BINS; COMPUTER VISION; HASH FUNCTIONS; IMAGE MATCHING; NETWORK LAYERS; OBJECT RECOGNITION;

DEEP LEARNING; DEEP NEURAL NETWORKS; DESCRIPTORS; DISTRIBUTED CODES; LINEAR HASH FUNCTION; RANDOM PROJECTIONS; VISUAL ANALYSIS; VISUAL OBJECTS;

PATTERN RECOGNITION;

EID: 84986249698     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.133     Document Type: Conference Paper

References (54)

1. A. Alahi, R. Ortiz, and P. Vandergheynst. Freak: Fast retina keypoint. In Proc. CVPR, 2012.
2. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In Proc. FOCS, 2006.
3. V. Balntas, L. Tang, and K. Mikolajczyk. Bold-binary online learned descriptor for efficient image matching. In Proc. CVPR, 2015.
4. M. Brown, G. Hua, and S. Winder. Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell., 33 (1): 43-57, 2011.
5. M. Brown and D. G. Lowe. Automatic panoramic image stitching using invariant features. Int'l J. Computer Vision, 74 (1): 59-73, 2007.
6. M. Calonder, V. Lepetit, C. Strecha, and P. Fua. Brief: Binary robust independent elementary features. In Proc. ECCV, 2010.
7. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In Proc. BMVC, 2014.
8. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proc. CVPR, 2005.
9. B. Fan, Q. Kong, T. Trzcinski, Z. Wang, C. Pan, and P. Fua. Receptive fields selection for binary feature description. IEEE Trans. Image Proc., 23 (6): 2583-2595, 2014.
10. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proc. CVPR, 2003.
11. P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In Proc. ICCV, 2009.
12. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014.
13. Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In Proc. CVPR, 2011.
14. Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 35 (12): 2916-2929, 2013.
15. K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In Proc. ICCV, 2005.
16. K. He, F. Wen, and J. Sun. K-means hashing: An affinitypreserving quantization method for learning binary compact codes. In Proc. CVPR, 2013.
17. J.-P. Heo, Y. Lee, J. He, S.-F. Chang, and S.-E. Yoon. Spherical hashing. In Proc. CVPR, 2012.
18. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In Proc. ACM MM, 2014.
19. A. Krizhevsky. Learning multiple layers of features from tiny images, 2009.
20. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Proc. NIPS, 2012.
21. H. Lai, Y. Pan, Y. Liu, and S. Yan. Simultaneous feature learning and hash coding with deep neural networks. In Proc. CVPR, 2015.
22. S. Leutenegger, M. Chli, and R. Y. Siegwart. Brisk: Binary robust invariant scalable keypoints. In Proc. ICCV, 2011.
23. K. Lin, H.-F. Yang, J.-H. Hsiao, and C.-S. Chen. Deep learning of binary hash codes for fast image retrieval. In Proc. CVPR Workshops, 2015.
24. V. E. Liong, J. Lu, G. Wang, P. Moulin, and J. Zhou. Deep hashing for compact binary codes learning. In Proc. CVPR, 2015.
25. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proc. CVPR, 2014.
26. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int'l J. Computer Vision, 60 (2): 91-110, 2004.
27. K. Mikolajczyk and C. Schmid. Indexing based on scale invariant interest points. In Proc. ICCV, 2001.
28. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell., 27 (10): 1615-1630, 2005.
29. M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In Proc. CVPR, 2006.
30. M.-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Proc. ICVGIP, 2008.
31. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In Proc. CVPR, 2014.
32. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: An astounding baseline for recognition. In Proc. CVPR Workshops, 2014.
33. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. Orb: An efficient alternative to sift or surf. In Proc. ICCV, 2011.
34. R. Salakhutdinov and G. E. Hinton. Semantic hashing. Int. J. Approx. Reasoning, 50 (7): 969-978, 2009.
35. G. Shakhnarovich. Learning task-specific similarity. PhD thesis, Massachusetts Institute of Technology, 2005.
36. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Proc. ICLR, 2015.
37. A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22 (12): 1349-1380, 2000.
38. C. Strecha, A. M. Bronstein, M. M. Bronstein, and P. Fua. Ldahash: Improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell., 34 (1): 66-78, 2012.
39. T. Trzcinski, M. Christoudias, P. Fua, and V. Lepetit. Boosting binary keypoint descriptors. In Proc. CVPR, 2013.
40. T. Trzcinski, M. Christoudias, and V. Lepetit. Learning image descriptors with boosting. IEEE Trans. Pattern Anal. Mach. Intell., 37 (3): 597-610, 2015.
41. T. Trzcinski and V. Lepetit. Efficient discriminative projections for compact binary descriptors. In Proc. ECCV, 2012.
42. M. Vidal-Naquet and S. Ullman. Object recognition with informative features and linear classification. In Proc. ICCV, 2003.
43. P. Viola and M. Jones. Robust real-time object detection. Int'l J. Computer Vision, 4: 51-52, 2001.
44. J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li. Deep learning for content-based image retrieval: A comprehensive study. In Proc. ACM MM, 2014.
45. J. Wang, S. Kumar, and S. Chang. Semi-supervised hashing for scalable image retrieval. In Proc. CVPR, 2010.
46. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In Proc. NIPS, 2008.
47. R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan. Supervised hashing for image retreieval via image representation learning. In Proc. AAAI, 2014.
48. X. Xu, L. Tian, J. Feng, and J. Zhou. Osri: A rotationally invariant binary descriptor. IEEE Trans. Image Proc., 23 (7): 2983-2995, 2014.
49. H.-F. Yang, K. Lin, and C.-S. Chen. Supervised learning of semantics-preserving hashing via deep neural networks for large-scale image search. ArXiv preprint arXiv: 1507. 00101, 2015.
50. X. Yang and K.-T. Cheng. Ldb: An ultra-fast feature for scalable augmented reality on mobile devices. In Proc. ISMAR, 2012.
51. X. Yang and K.-T. Cheng. Local difference binary for ultrafast and distinctive feature description. IEEE Trans. Pattern Anal. Mach. Intell., 36 (1): 188-194, 2014.
52. J. Zhang, M. Marszaek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. Int'l J. Computer Vision, 73 (2): 213-238, 2007.
53. S. Zhang, Q. Tian, Q. Huang, W. Gao, and Y. Rui. Usb: ultrashort binary descriptor for fast visual matching and retrieval. IEEE Trans. Image Proc., 23 (8): 3671-3683, 2014.
54. L. Zheng, S. Wang, and Q. Tian. Coupled binary embedding for large-scale image retrieval. IEEE Trans. Image Proc., 23 (8): 3368-3380, 2014.