Scalable graph hashing with feature transformation

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2015-January, Issue , 2015, Pages 2248-2254

  Jiang, Qing Yuan ^a   Li, Wu Jun ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIGITAL STORAGE; HASH FUNCTIONS; LINEAR TRANSFORMATIONS; NEAREST NEIGHBOR SEARCH;

APPROXIMATE NEAREST NEIGHBORS (ANN); FEATURE TRANSFORMATIONS; LEARNING PROCEDURES; NEIGHBORHOOD STRUCTURE; REAL APPLICATIONS; SEARCH IN BIG DATUM; SEQUENTIAL LEARNING; STATE-OF-THE-ART METHODS;

BIG DATA;

EID: 84949775463     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Alexandr Andoni and Piotr Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications on ACM, 51(1):117-122, 2008.
2. Alexandr Andoni. Nearest Neighbor Search: The Old, The New, and The Impossible. PhD thesis, Massachusetts Institute of Technology, 2009.
3. Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the Annual Symposium on Computational Geometry, pages 253-262, 2004.
4. Aristides Gionis, Piotr Indyk, and Rajeev Motwani. Similarity search in high dimensions via hashing. In Proceedings of the International Conference on Very Large Data Bases, pages 518-529, 1999.
5. Yunchao Gong and Svetlana Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 817-824, 2011.
6. Mark J. Huiskes, B. Thomee, and Michael S. Lew. New trends and ideas in visual concept detection: The mir flickr retrieval evaluation initiative. In Proceedings of the ACM International Conference on Multimedia Information Retrieval, pages 527-536, 2010.
7. Piotr Indyk and Rajeev Motwani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proceedings of the Annual ACM Symposium on Theory of Computing, pages 604-613, 1998.
8. Weihao Kong and Wu-Jun Li. Isotropic hashing. In Proceedings of the Advances in Neural Information Processing Systems, pages 1655-1663, 2012.
9. Brian Kulis and Trevor Darrell. Learning to hash with binary reconstructive embeddings. In Proceedings of the Advances in Neural Information Processing Systems, pages 1042-1050, 2009.
10. Brian Kulis and Kristen Grauman. Kernelized locality-sensitive hashing for scalable image search. In Proceedings of the IEEE International Conference on Computer Vision, pages 2130-2137, 2009.
11. Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, and David Suter. Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1971-1978, 2014.
12. Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Hashing with graphs. In Proceedings of the International Conference on Machine Learning, 2011.
13. Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. Supervised hashing with kernels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2074-2081, 2012.
14. Wei Liu, Cun Mu, Sanjiv Kumar, and Shih-Fu Chang. Discrete graph hashing. In Proceedings of the Advances in Neural Information Processing Systems, pages 3419-3427, 2014.
15. Mohammad Norouzi and David J. Fleet. Minimal loss hashing for compact binary codes. In Proceedings of the International Conference on Machine Learning, pages 353-360, 2011.
16. Anshumali Shrivastava and Ping Li. Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips). In Proceedings of the Advances in Neural Information Processing Systems, pages 2321-2329, 2014.
17. Jingkuan Song, Yang Yang, Yi Yang, Zi Huang, and Heng Tao Shen. Inter-media hashing for large-scale retrieval from heterogeneous data sources. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 785-796, 2013.
18. Jun Wang, Ondrej Kumar, and Shih-Fu Chang. Semi-supervised hashing for scalable image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3424-3431, 2010.
19. Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Sequential projection learning for hashing with compact codes. In Proceedings of the International Conference on Machine Learning, pages 1127-1134, 2010.
20. Yair Weiss, Antonio Torralba, and Robert Fergus. Spectral hashing. In Proceedings of the Advances in Neural Information Processing Systems, pages 1753-1760, 2008.
21. Bin Xu, Jiajun Bu, Yue Lin, Chun Chen, Xiaofei He, and Deng Cai. Harmonious hashing. In Proceedings of the International Joint Conference on Artificial Intelligence, 2013.
22. Dongqing Zhang and Wu-Jun Li. Large-scale supervised multimodal hashing with semantic correlation maximization. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2177-2183, 2014.
23. Peichao Zhang, Wei Zhang, Wu-Jun Li, and Minyi Guo. Supervised hashing with latent factor models. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 173-182, 2014.
24. Yi Zhen and Dit-Yan Yeung. A probabilistic model for multimodal hash function learning. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 940-948, 2012.
25. Xiaofeng Zhu, Zi Huang, Heng Tao Shen, and Xin Zhao. Linear cross-modal hashing for efficient multimedia search. In Proceedings of the ACM International Conference on Multimedia, pages 143-152, 2013.