NeTra: a toolbox for navigating large image databases
Multimedia Systems - Special issue on video content based retrieval
Locating and Recognizing Text in WWW Images
Information Retrieval
Indexing Flower Patent Images Using Domain Knowledge
IEEE Intelligent Systems
Finding cohesive clusters for analyzing knowledge communities
Knowledge and Information Systems
Analyzing knowledge communities using foreground and background clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Central object extraction for object-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Natural / man-made object classification based on gabor characteristics
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
On object classification: artificial vs. natural
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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This research addresses the issue of automatically segmenting color images into foreground (F) and back-ground (B) regions with the assumption that background regions are relatively smooth but may have gradually varying colors or be slightly textured. A multi-level segmentation scheme is used that involves color clustering, unsupervised segmentation using MDL (minimum description length) principle, edge-based F/B separation, and integrated F/B segmentation. The approach has been tested on more than 100 images. Some of the experimental results are presented.