Abstract
Hashing-based approximate nearest-neighbor search may well realize scalable content-based image retrieval. The existing semantic-preserving hashing methods leverage the labeled data to learn a fixed set of semantic-aware hash functions. However, a fixed hash function set is unable to well encode all semantic information simultaneously, and ignores the specific user's search intention conveyed by the query. In this article, we propose a query-adaptive hashing method which is able to generate the most appropriate binary codes for different queries. Specifically, a set of semantic-biased discriminant projection matrices are first learnt for each of the semantic concepts, through which a semantic-adaptable hash function set is learnt via a joint sparsity variable selection model. At query time, we further use the sparsity representation procedure to select the most appropriate hash function subset that is informative to the semantic information conveyed by the query. Extensive experiments over three benchmark image datasets well demonstrate the superiority of our proposed query-adaptive hashing method over the state-of-the-art ones in terms of retrieval accuracy.
- Bosch, A., Zisserman, A., and Muñoz, X. 2006. Scene classification via plsa. In Proceedings of the European Conference on Computer Vision. 517--530. Google Scholar
Digital Library
- Boyd, S. and Vandenberghe, L. 2004. Convex Optimization. Cambridge University Press. Google Scholar
Digital Library
- Candès, E. and Romberg, J. 2007. L1-magic. http://users.ece.gatech.edu/~justin/l1magic/.Google Scholar
- Fei-Fei, L., Robert, F., and Pietro, P. 2007. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vision Image Understand. 106, 1, 59--70. Google Scholar
Digital Library
- Indyk, P. and Motwani, R. 1998. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proceedings of the Annual ACM Symposium on Theory of Computing. 604--613. Google Scholar
Digital Library
- Jégou, H., Amsaleg, L., Schmid, C., and Gros, P. 2008. Query-Adaptive locality sensitive hashing. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 12--15.Google Scholar
- Jia, D., Wei, D., Richard, S., Li-jia, L., Kai, L., and Fei-Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 248--255.Google Scholar
- Jiang, Y.-G., Wang, J., and Chang, S.-F. 2011. Lost in binarization: Query-adaptive ranking for similar image search with compact codes. In Proceedings of the ACM International Conference on Multimedia Retrieval. Google Scholar
Digital Library
- Kulis, B. and Darrell, T. 2009. Learning to hash with binary reconstructive embeddings. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems. 1042--1050.Google Scholar
- Kulis, B. and Grauman, K. 2009. Kernelized locality-sensitive hashing for scalable image search. In Proceedings of the International Conference on Computer Vision. 2130--2137.Google Scholar
- Kulis, B., Jain, B., and Grauman, K. 2009. Fast similarity search for learned metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31, 12, 2143--2157. Google Scholar
Digital Library
- Lee, D., Ke, Q., and Isard, M. 2010. Partition min-hash for partial duplicate image discovery. In Proceedings of the European Conference on Computer Vision. 648--662. Google Scholar
Digital Library
- Raginsky, M. and Lazebnik, S. 2009. Locality-sensitive binary codes from shift-invariant kernels. In Proceedings of 23rd Annual Conference on Neural Information Processing Systems. 1509--1517.Google Scholar
- Rui, Y., Huang, T., and Chang, S.-F. 1999. Image retrieval: Current techniques, promising directions, and open issues. J. Vis. Comm. Image Represent. 10, 1, 39--62.Google Scholar
Digital Library
- Salakhutdinov, R. and Hinton, G. 2009. Semantic hashing. Int. J. Approx. Reasoning 50, 7, 969--978. Google Scholar
Digital Library
- Shakhnarovich, G., Viola, P., and Darrell, T. 2003. Fast pose estimation with parameter-sensitive hashing. In Proceedings of the International Conference on Computer Vision. 750--757. Google Scholar
Digital Library
- Shakhnarovich, R. and Hinton, G. 2007. Learning a nonlinear embedding by preserving class neighbourhood structure. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 1--8.Google Scholar
- Tseng, P. 2008. On accelerated proximal gradient methods for convex-concave optimization. SIAM J. Optim. (to appear).Google Scholar
- Wang, J., Kumar, S., and Chang, S.-F. 2010. Semi-Supervised hashing for scalable image retrieval. In Proceedings of the International Conference on Computer Vision. 3424--3431.Google Scholar
- Weiss, Y., Torralba, A., and Fergus, R. 2001. Small sample learning during multimedia retrieval using biasmap. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 11--17.Google Scholar
- Weiss, Y., Torralba, A., and Fergus, R. 2008. Spectral hashing. In Advances in Neural Information Processing. 1753--1760.Google Scholar
- Winder, S. and Brown, M. 2007. Learning local image descriptors. In Proceedings of the International Conference on Computer Vision. 1--8.Google Scholar
Index Terms
Image retrieval with query-adaptive hashing
Recommendations
Manhattan hashing for large-scale image retrieval
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrievalHashing is used to learn binary-code representation for data with expectation of preserving the neighborhood structure in the original feature space. Due to its fast query speed and reduced storage cost, hashing has been widely used for efficient ...
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrievalSimilarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) ...
Neighborhood Discriminant Hashing for Large-Scale Image Retrieval
With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its ...






Comments