International Journal of Computer Vision
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Learning Schemes for Multimedia Information Retrieval
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Support vector machines for region-based image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Two Step Relevance Feedback for Semantic Disambiguation in Image Retrieval
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Nearest neighbor editing aided by unlabeled data
Information Sciences: an International Journal
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In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). Since users are usually unwilling to provide much feedback, the insufficiency of training samples limits the success of relevance feedback. In this paper, we propose two strategies to tackle this problem: (i) to make relevance feedback more informative by presenting representative images for users to label; (ii) to make use of unlabeled data in the training process. As a result, an active feedback framework is proposed, consisting of two components, representative image selection and label propagation. For practical implementation of this framework, we develop two coupled algorithms corresponding to the two components, namely, overlapped subspace clustering and multi-subspace label propagation. Experimental results on a very large-scale image collection demonstrated the high effectiveness of the proposed active feedback framework.