IISM: an image internal semantic model for image database based on relevance feedback Export

@proceedings{citeulike:1696838, abstract = {A semantic model - IISM (image internal semantic model) is introduced. Unlike other semantic extracting methods, IISM extracts the semantic information not by image segmentation and image understanding, but by analyzing relevance feedback image retrieval results. For relevance feedback image retrieval system, the images relevant to query are pointed as positive example, otherwise the images irrelevant to query are pointed as negative examples. It is assumed that these positive examples are related in semantic content. IISM computes comprehensive pair-wise mutual information for all images through analyzing the results of relevance feedback image retrieval. An association with a high mutual information means that one image is semantically associated with another. Semantic retrieval and clustering is carried out based on these association relationships.}, author = {Duan, Lijuan and Gao, Wen}, booktitle = {Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on}, citeulike-article-id = {1696838}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1241258}, journal = {Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on}, pages = {528--531}, posted-at = {2007-09-26 11:22:47}, priority = {4}, title = {IISM: an image internal semantic model for image database based on relevance feedback}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1241258}, year = {2003} }

TY - CONF ID - citeulike:1696838 L3 - citeulike-article-id:1696838 N2 - A semantic model - IISM (image internal semantic model) is introduced. Unlike other semantic extracting methods, IISM extracts the semantic information not by image segmentation and image understanding, but by analyzing relevance feedback image retrieval results. For relevance feedback image retrieval system, the images relevant to query are pointed as positive example, otherwise the images irrelevant to query are pointed as negative examples. It is assumed that these positive examples are related in semantic content. IISM computes comprehensive pair-wise mutual information for all images through analyzing the results of relevance feedback image retrieval. An association with a high mutual information means that one image is semantically associated with another. Semantic retrieval and clustering is carried out based on these association relationships. JF - Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on EP - 531 TI - IISM: an image internal semantic model for image database based on relevance feedback T2 - Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on SP - 528 AU - Duan, Lijuan AU - Gao, Wen PY - 2003/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1241258 ER -