Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Reranking Methods for Visual Search
IEEE MultiMedia
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Noise resistant graph ranking for improved web image search
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Joint semantics and feature based image retrieval using relevance feedback
IEEE Transactions on Multimedia
IntentSearch: Capturing User Intention for One-Click Internet Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised hashing with kernels
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Although text-based image search engines are popular for ranking images of user's interest, the state-of-the-art ranking performance is still far from satisfactory. One major issue comes from the visual similarity metric used in the ranking operation, which depends solely on visual features. To tackle this issue, one feasible method is to incorporate semantic concepts, also known as image attributes, into image ranking. However, the optimal combination of visual features and image attributes remains unknown. In this paper, we propose a query-dependent image reranking approach by leveraging the higher level attribute detection among the top returned images to adapt the dictionary built over the visual features to a query-specific fashion. We start from offline learning transposition probabilities between visual codewords and attributes, then utilize the probabilities to online adapt the dictionary, and finally produce a query-dependent and semantics-induced metric for image ranking. Extensive evaluations on several benchmark image datasets demonstrate the effectiveness and efficiency of the proposed approach in comparison with state-of-the-arts.