Bayesian Relevance Feedback for Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Multi-class relevance feedback content-based image retrieval
Computer Vision and Image Understanding
Robust Real-Time Face Detection
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Content-based image retrieval (CBIR) is an effective approach for obtaining desired image, however, due to the semantic gap between low-level visual features and high-level concept of image, CBIR system of state-of-the-art always can't achieve satisfying retrieval performance. In this paper, we propose a novel CBIR system framework. In order to bridge the semantic gap, the mechanism of relevance feedback is involved in the system. More various features are included at low level, which can provide more abundant image content description. A bi-coded chromosome based genetic algorithm is performed to obtain optimal features and relevant optimal weights based on users' relevance feedback. With the optimal feature set and optimal weights, the similarity between image in original searching results and query image is considered to be the main factor of rank score.