A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Intra-dimensional feature diagnosticity in the Fuzzy Feature Contrast Model
Image and Vision Computing
Semi-automatic dynamic auxiliary-tag-aided image annotation
Pattern Recognition
IEEE Transactions on Image Processing
A fuzzy combined learning approach to content-based image retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Adaptive color feature extraction based on image color distributions
IEEE Transactions on Image Processing
Content-based image retrieval with relevance feedback using random walks
Pattern Recognition
Improved adaboost-based image retrieval with relevance feedback via paired feature learning
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Using segmented objects in ostensive video shot retrieval
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme
Information Sciences: an International Journal
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Mammogram Retrieval: Image Selection Strategy of Relevance Feedback for Locating Similar Lesions
International Journal of Digital Library Systems
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From a perceptual standpoint, the subjectivity inherent in understanding and interpreting visual content in multimedia indexing and retrieval motivates the need for online interactive learning. Since efficiency and speed are important factors in interactive visual content retrieval, most of the current approaches impose restrictive assumptions on similarity calculation and learning algorithms. Specifically, content-based image retrieval techniques generally assume that perceptually similar images are situated close to each other within a connected region of a given space of visual features. This paper proposes a novel method for interactive image retrieval using query feedback. Query feedback learns the user query as well as the correspondence between high-level user concepts and their low-level machine representation by performing retrievals according to multiple queries supplied by the user during the course of a retrieval session. The results presented in this paper demonstrate that this algorithm provides accurate retrieval results with acceptable interaction speed compared to existing methods.