OCRS: an interactive object-based image clustering and retrieval system
Multimedia Tools and Applications
Design and implementation of a fuzzy-modified ant colony hardware structure for image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An endmember-based distance for content based hyperspectral image retrieval
Pattern Recognition
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
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A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework is used to achieve this. It seamlessly incorporates multiple instance learning for relevant feedback to discover users concept patterns-especially in the region of greatest user interest. It also maps the local feature vector of that region to the high-level concept pattern. This underlying mapping can be progressively discovered through feedback and learning. The user guides the retrieval systems learning process using his/her focus of attention. Retrieval performance is tested to establish the feasibility and effectiveness of the proposed learning and retrieval framework