Combining supervised learning with color correlograms for content-based image retrieval
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
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
iFind—a system for semantics and feature based image retrieval over Internet
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Web mining for web image retrieval
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
A Relevance Feedback Architecture for Content-based Multimedia Information Retrieval Systems
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
IEEE Transactions on Image Processing
The Truth about Corel - Evaluation in Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
An efficient memorization scheme for relevance feedback in image retrieval
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
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A bigram correlation model for image retrieval is proposed, which captures the semantic relationship among images in a database from simple statistics of users' relevance feedback information. It is used in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images are very promising.