A biased minimax probability machine-based scheme for relevance feedback in image retrieval
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AbstractIn recent years, minimax probability machines (MPMs) have demonstrated excellent performance in a variety of pattern recognition problems. At the same time various machine learning methods have been applied on relevance feedback tasks in content-based image retrieval (CBIR). One of the problems in typical techniques for relevance feedback is that they treat the relevant feedback and irrelevant feedback equally. Since the negative instances largely outnumber the positive instances, the assumption that they are balanced is incorrect as the data are biased. In this paper we study how biased minimax probability machine (BMPM), a variation of MPM, can be applied for relevance feedback in image retrieval tasks. Different from previous methods, this model directly controls the accuracy of classification of the future data to construct biased classifiers. Hence, it provides a rigorous treatment on imbalanced dataset. Mathematical formulation and explanations are provided to demonstrate the advantages. Experiments are conducted to evaluate the performance of our proposed framework, in which encouraging and promising experimental results are obtained. Crown Copyright (c) 2008 Published by Elsevier B.V. All rights reserved.
All Author(s) ListPeng X, King I
Journal nameNeurocomputing
Year2009
Month3
Day1
Volume Number72
Issue Number7-9
PublisherElsevier
Pages2046 - 2051
ISSN0925-2312
eISSN1872-8286
LanguagesEnglish-United Kingdom
KeywordsBiased minimax probability machine; Content-based image retrieval; Relevance feedback
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Last updated on 2020-28-05 at 02:27