Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Convex Optimization
Computer Vision and Image Understanding
International Journal of Approximate Reasoning
Fast Similarity Search for Learned Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partition min-hash for partial duplicate image discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Lost in binarization: query-adaptive ranking for similar image search with compact codes
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Hi-index | 0.00 |
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.