A biased minimax probability machine-based scheme for relevance feedback in image retrieval

In 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.