Similarity measures

by: S Santini, R Jain

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 21, No. 9. (1999), pp. 871-883.

View FullText article

DOI, IEEE Explore

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

thsant, mhaller, nikux, video_retrieval (group), javimol, qwermish

Find related articles with these CiteULike tags

computation, image_retrieval, imaging, mathematics, model, no-tag, video_retrieval

Abstract

With complex multimedia data, we see the emergence of database systems in which the fundamental operation is similarity assessment. Before database issues can be addressed, it is necessary to give a definition of similarity as an operation. We develop a similarity measure, based on fuzzy logic, that exhibits several features that match experimental findings in humans. The model is dubbed fuzzy feature contrast (FFC) and is an extension to a more general domain of the feature contrast model due to Tversky (1977). We show how the FFC model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties

@article{citeulike:612074, abstract = {With complex multimedia data, we see the emergence of database systems in which the fundamental operation is similarity assessment. Before database issues can be addressed, it is necessary to give a definition of similarity as an operation. We develop a similarity measure, based on fuzzy logic, that exhibits several features that match experimental findings in humans. The model is dubbed fuzzy feature contrast (FFC) and is an extension to a more general domain of the feature contrast model due to Tversky (1977). We show how the FFC model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties}, author = {Santini, S. and Jain, R. }, citeulike-article-id = {612074}, doi = {10.1109/34.790428}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, keywords = {computation, imaging, mathematics, model}, number = {9}, pages = {871--883}, posted-at = {2008-07-11 11:05:13}, priority = {0}, title = {Similarity measures}, url = {http://dx.doi.org/10.1109/34.790428}, volume = {21}, year = {1999} }

RIS record

TY - JOUR ID - citeulike:612074 L3 - citeulike-article-id:612074 TI - Similarity measures JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on VL - 21 IS - 9 SP - 871 EP - 883 N2 - With complex multimedia data, we see the emergence of database systems in which the fundamental operation is similarity assessment. Before database issues can be addressed, it is necessary to give a definition of similarity as an operation. We develop a similarity measure, based on fuzzy logic, that exhibits several features that match experimental findings in humans. The model is dubbed fuzzy feature contrast (FFC) and is an extension to a more general domain of the feature contrast model due to Tversky (1977). We show how the FFC model can be used to model similarity assessment from fuzzy judgment of properties, and we address the use of fuzzy measures to deal with dependencies among the properties KW - computation KW - imaging KW - mathematics KW - model AU - Santini, S AU - Jain, R PY - 1999/// UR - http://dx.doi.org/10.1109/34.790428 ER -

RIS BibTeX