Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
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MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
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International Journal of Computer Vision
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Proceedings of the international conference on Multimedia
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ACM Transactions on Intelligent Systems and Technology (TIST)
Describable Visual Attributes for Face Verification and Image Search
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Towards multi-semantic image annotation with graph regularized exclusive group lasso
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Image ranking and retrieval based on multi-attribute queries
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Tag localization with spatial correlations and joint group sparsity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Combining attributes and Fisher vectors for efficient image retrieval
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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IEEE Transactions on Multimedia
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
Utilizing Related Samples to Enhance Interactive Concept-Based Video Search
IEEE Transactions on Multimedia
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
WhittleSearch: Image search with relative attribute feedback
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Proceedings of the 21st ACM international conference on Multimedia
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This work presents a new interactive Content Based Image Retrieval (CBIR) scheme, termed Attribute Feedback (AF). Unlike traditional relevance feedback purely founded on low-level visual features, the Attribute Feedback system shapes users' information needs more precisely and quickly by collecting feedbacks on intermediate level semantic attributes. At each interactive iteration, AF first determines the most informative binary attributes for feedbacks, preferring the attributes that frequently (rarely) appear in current search results but are unlikely (likely) to be users' interest. The binary attribute feedbacks are then augmented by a new type of attributes, "affinity attributes", each of which is off-line learnt to describe the distance between user's envisioned image(s) and a retrieved image with respect to the corresponding affinity attribute. Based on the feedbacks on binary and affinity attributes, the images in corpus are further re-ranked towards better fitting the users' information needs. Extensive experiments on two real-world image datasets well demonstrate the superiority of the proposed scheme over other state-of-the-art relevance feedback based CBIR solutions.