Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
A relevance feedback mechanism for content-based image retrieval
Information Processing and Management: an International Journal
Adaptive nearest neighbor search for relevance feedback in large image databases
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
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)
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Visual information retrieval using synthesized imagery
Proceedings of the 6th ACM international conference on Image and video retrieval
Performance evaluation of relevance feedback methods
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
An artificial imagination for interactive search
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
Image searching and browsing by active aspect-based relevance learning
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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We analyze the special structure of the relevance feedback learning problem, focusing particularly on the effects of image selection by partial relevance on the clustering behavior of feedback examples. We propose a scheme, aspect-based relevance learning, which guarantees that feedback on feature values is accepted only once evidential support that the feedback was intended by the user is sufficiently strong. The scheme additionally allows for natural simulation of the relevance feedback process. By means of simulation we analyze retrieval performance, search regularity and sensitivity to feature errors.