Joint querying and relevance feedback scheme for an on-line image retrieval system
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
On-line content-based image retrieval system using joint querying and relevance feedback scheme
WSEAS Transactions on Computers
Semantic annotation of image groups with self-organizing maps
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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In this paper, we study computational models and techniques to merge textual and image features to classify images on the World Wide Web (WWW). A vector-based framework is used to index images on the basis of textual, pictorial and composite (textual-pictorial) information. The scheme makes use of weighted document terms and color invariant image features to obtain a high-dimensional image descriptor in vector form to be used as an index. Experiments are conducted on a representative set of more than 100.000 images down loaded from the WWW together with their associated text. Performance evaluations are reported on the accuracy of merging textual and pictorial information for image classification.