Compact hashing with joint optimization of search accuracy and time

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

Volumn , Issue , 2011, Pages 753-760

  He, Junfeng ^a   Chang, Shih Fu ^a   Radhakrishnan, Regunathan ^b   Bauer, Claus ^b  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b DOLBY LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); HASH FUNCTIONS; LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH;

DIVERSE DATA SETS; HASHING ALGORITHMS; JOINT OPTIMIZATION; LARGE-SCALE MACHINE LEARNING; NEAREST NEIGHBORHOOD; SIMILARITY FUNCTIONS; SIMILARITY SEARCH; VISION APPLICATIONS;

COMPUTER VISION;

EID: 80052901697     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995518     Document Type: Conference Paper

References (23)

1. M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni. Locality sensitive hashing scheme based on p-Stable distributions. In Proceedings of Symposium on Computational Geometry (SOCG), 2004.
2. K. Grauman and T. Darrell. Pyramid match hashing: sub-Linear time indexing over partial correspondences. In Proceedings of CVPR, 2007.
3. P. Jain, B. Kulis, and K. Grauman. Fast image search for learned metrics. In Proceedings of CVPR, 2008.
4. R. Salakhutdinov and G. Hinton. Semantic hashing. In Proceedings of SIGIR, 2007.
5. R. Salakhutdinov and G. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. In Proceedings of AI and Statistics, 2007.
6. A. Torralba, R. Fergus, and Y. Weiss. Small Codes and Large Image Databases for Recognition. In Proceedings of CVPR, 2008.
7. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In Proceedings of NIPS, 2008.
8. Brian Kulis and Kristen Grauman. Kernelized Locality-Sensitive Hashing for Scalable Image Search. In Proceedings of ICCV, 2009.
9. Brian Kulis and Trevor Darrell. Learning to hash with binary reconstructive embeddings. In Proceedings of NIPS, 2009.
10. M. Raginsky and S. Lazebnik. Locality sensitive binary codes from shift-invariant kernels. In Proceedings of NIPS, 2009.
11. Ruei-Sung Lin David A. Ross Jay Yagnik. SPEC Hashing: Similarity Preserving algorithm for Entropy-based Coding In Proceedings of CVPR, 2010.
12. Yadong Mu, Jialie Shen, Shuicheng Yan. Weakly-Supervised Hashing in Kernel Space In Proceedings of CVPR, 2010.
13. Junfeng He, Wei Liu and Shih-Fu Chang. Scalable Similarity Search with Optimized Kernel Hashing In Proceedings of KDD, 2010.
14. Shumeet Baluja and Michele Covell. Learning to hash: forgiving hash functions and applications. Data Mining and Knowledge Discovery, 2008.
15. Aapo Hyvarinen and Erkki Oja. Independent Component Analysis: Algorithms and Applications. Neural Networks, page 411-430, 2000.
16. Alper T. Erdogan. On the Convergence of ICA Algorithms With Symmetric Orthogonalization. IEEE Transactions on Signal Processing, 2009.
17. Xiaofei He, and Partha Niyogi. Locality Preserving Projections. In Proceedings of NIPS, 2003.
18. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal on Computer Vision, 2001.
19. Christopher Williams, Matthias Seeger. Using the nystrom method to speed up kernel machines. In Proceedings of NIPS, 2001.
20. Ali Rahimi and Benjamin Recht. Random Features for Large-Scale Kernel Machines. In Proceedings of NIPS, 2007.
21. S. Lazebnik, C. Schmid and J. Ponce. Beyond bags of features, spatial pyramid matching for recognizing natural scene categories. In Proceedings of CVPR, 2006.
22. S. Winder and M. Brown. Learning Local Image Descriptors. In Proceedings of Computer Vision and Pattern Recognition, 2007.
23. Nino Shervashidze, Karsten M. Borgwardt. Fast subtree kernels on graphs. In Proceedings of NIPS, 2009.