期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 52, 期 8, 页码 2322-2336出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2004.831125
关键词
Bayesian methods; color and texture; expectation-maximization; feature selection; image retrieval; image similarity; minimum probability of error; mixture models; multiresolution; optimal retrieval systems; wrapper methods
We address the design of optimal architectures for image retrieval from large databases. Minimum probability of error (MPE) is adopted as the optimality criterion and retrieval formulated as a problem of statistical classification. The probability of retrieval error is lower- and upper-bounded by functions of the Bayes and density estimation errors, and the impact of the components of the retrieval architecture (namely, the feature transformation and density estimation) on these bounds is characterized. This characterization suggests interpreting the search for the MPE feature set as the search for the minimum of the convex hull of a collection of curves of probability of error versus feature space dimension. A new algorithm for MPE feature design, based on a dictionary of empirical feature sets and the wrapper model for feature selection, is proposed. It is shown that, unlike traditional feature selection techniques, this algorithm scales to problems containing large numbers of classes. Experimental evaluation reveals that the MPE architecture is at least as good as popular empirical solutions on the narrow domains where these perform best but significantly outperforms them outside these domains.
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