Category-based image retrieval Export

@inproceedings{citeulike:1304947, abstract = {This work presents a novel approach to content-based image retrieval in categorical multimedia databases. The images are indexed using a combination of text and content descriptors. The categories are viewed as semantic clusters of images and are used to confine the search space. Keywords are used to identify candidate categories. Content-based retrieval is performed in these categories using multiple image features. Relevance feedback is used to learn the user's intent-query specification and feature-weighting-with minimal user-interface abstraction. The method is applied to a large number of images collected from a popular categorical structure on the World Wide Web. Results show that efficient and accurate performance is achievable by exploiting the semantic classification represented by the categories. The relevance feedback loop allows the content descriptor weightings to be determined without exposing the calculations to the user}, author = {Newsam, S. and Sumengen, B. and Manjunath, B. S.}, citeulike-article-id = {1304947}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=958189}, journal = {Image Processing, 2001. Proceedings. 2001 International Conference on}, keywords = {cbir, future-of-search, text-retrieval, to-read}, pages = {596--599 vol.3}, posted-at = {2007-05-18 08:55:28}, priority = {2}, title = {Category-based image retrieval}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=958189}, volume = {3}, year = {2001} }

TY - CONF ID - citeulike:1304947 L3 - citeulike-article-id:1304947 N2 - This work presents a novel approach to content-based image retrieval in categorical multimedia databases. The images are indexed using a combination of text and content descriptors. The categories are viewed as semantic clusters of images and are used to confine the search space. Keywords are used to identify candidate categories. Content-based retrieval is performed in these categories using multiple image features. Relevance feedback is used to learn the user's intent-query specification and feature-weighting-with minimal user-interface abstraction. The method is applied to a large number of images collected from a popular categorical structure on the World Wide Web. Results show that efficient and accurate performance is achievable by exploiting the semantic classification represented by the categories. The relevance feedback loop allows the content descriptor weightings to be determined without exposing the calculations to the user JF - Image Processing, 2001. Proceedings. 2001 International Conference on EP - 599 vol.3 TI - Category-based image retrieval VL - 3 SP - 596 KW - cbir KW - future-of-search KW - text-retrieval KW - to-read AU - Newsam, S AU - Sumengen, B AU - Manjunath, BS PY - 2001/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=958189 ER -