A probabilistic framework for multimodal retrieval using integrative Indian buffet process

Advances in Neural Information Processing Systems

Volumn 3, Issue January, 2014, Pages 2384-2392

  Ozdemir, Bahadir ^a   Davis, Larry S ^b  

^a University of Maryland   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDBACK; LEARNING ALGORITHMS;

EXTENSION MODELS; MULTI-MODAL DATASET; MULTIMODAL DATASETS; PROBABILISTIC FRAMEWORK; PROBABILISTIC RETRIEVAL; RELEVANCE FEEDBACK; RETRIEVAL PROCEDURES; STATE-OF-THE-ART ALGORITHMS;

INFORMATION SCIENCE;

EID: 84937943679     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In Advances in Neural Information Processing Systems 21, pages 1753-1760, 2009.
2. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008, pages 1-8, June 2008.
3. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In Proceedings of the 25th International Conference on Very Large Data Bases, VLDB '99, pages 518-529, 1999.
4. B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In IEEE 12th International Conference on Computer Vision, 2009, pages 2130-2137, Sept 2009.
5. M. Raginsky and S. Lazebnik. Locality-sensitive binary codes from shift-invariant kernels. In Advances in Neural Information Processing Systems 22, pages 1509-1517, 2009.
6. Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, pages 817-824, June 2011.
7. J.-P. Heo, Y. Lee, J. He, S.-F. Chang, and S.-E. Yoon. Spherical hashing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pages 2957-2964, June 2012.
8. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1349-1380, Dec 2000.
9. M. M. Bronstein, E. M. Bronstein, F. Michel, and N. Paragios. Data fusion through cross-modality metric learning using similarity-sensitive hashing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pages 3594-3601, June 2010.
10. S. Kumar and R. Udupa. Learning hash functions for cross-view similarity search. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Two, IJCAI'11, pages 1360-1365, 2011.
11. Y. Zhen and D.-Y. Yeung. Co-regularized hashing for multimodal data. In Advances in Neural Information Processing Systems 25, pages 1376-1384, 2012.
12. Y. Zhen and D.-Y. Yeung. A probabilistic model for multimodal hash function learning. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pages 940-948, 2012.
13. Nitish Srivastava and Ruslan Salakhutdinov. Multimodal learning with deep boltzmann machines. In Advances in Neural Information Processing Systems 25, pages 2222-2230, 2012.
14. M. Rastegari, J. Choi, S. Fakhraei, H. Daume III, and L. S. Davis. Predictable Dual-View Hashing. Proceedings of The 30th International Conference on Machine Learning, pages 1328-1336, 2013.
15. A. Sharma, A. Kumar, H. Daume III, and D. W. Jacobs. Generalized multiview analysis: A discriminative latent space. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pages 2160-2167, June 2012.
16. X. S. Zhou and T. S. Huang. Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems, 8(6):536-544, 2003.
17. Y. Yang, F. Nie, D. Xu, J. Luo, Y. Zhuang, and Y. Pan. A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4):723-742, April 2012.
18. T. L. Griffiths and Z. Ghahramani. Infinite latent feature models and the indian buffet process. In Advances in Neural Information Processing Systems 18, pages 475-482, 2005.
19. F. Doshi-Velez and Z. Ghahramani. Accelerated sampling for the indian buffet process. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pages 273-280, 2009.
20. A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth. Every picture tells a story: Generating sentences from images. In Proceedings of the 11th European Conference on Computer Vision: Part IV, ECCV'10, pages 15-29, Berlin, Heidelberg, 2010.
21. G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pages 2751-2758, June 2012.
22. D. Lin. An information-theoretic definition of similarity. In Proceedings of the Fifteenth International Conference on Machine Learning, ICML '98, pages 296-304, 1998.
23. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pages 3485-3492, June 2010.
24. H. Hotelling. Relations Between Two Sets of Variates. Biometrika, 28(3/4):321-377, December 1936.