A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
A statistical correlation model for image retrieval
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
PARAgrab: a comprehensive architecture for web image management and multimodal querying
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A comprehensive analysis for relevance feedback in CBIR system
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
An efficient memorization scheme for relevance feedback in image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Web image retrieval systems with automatic web image annotating techniques
WSEAS Transactions on Information Science and Applications
Comparison of Strategies Based on Evolutionary Computation for the Design of Similarity Functions
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Automatic web image annotation for image retrieval systems
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
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iFind© (v 1.0) is a web based image retrieval system developed at Microsoft Research China. It provides the functionalities of keyword based image search, query by image example, category based image browsing, relevance feedback, and semi-automatic image annotation. The key technology in the system is the integrated semantics and feature based image retrieval and relevance feedback approach, which will be presented in our paper in the ACM Multimedia 2000 Proceedings [1]. When the user provides feedback images, the system can refine the retrieval result based on the user's feedback. In the meantime, the system updates the annotation of feedback images by increasing the linkage to the positive examples' annotation and decreasing the linkage to the negative examples' annotation. The updated annotation can further help to improve image retrieval results of the system in later use.