Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimized Interaction Strategy for Bayesian Relevance Feedback
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A robust minimax approach to classification
The Journal of Machine Learning Research
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In recent years, Minimax Probability Machine (MPM) have demonstrated excellent performance in a variety of pattern recognition problems. At the same time various machine learning methods have been used 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. In other words, the negative instances largely outnumber the positive instances. Hence, the assumption that they are balanced is incorrect. In this paper we study how MPM can be applied to image retrieval, more precisely, Biased MPM during the relevance feedback iterations. We formulate the relevance feedback based on a modified MPM called Biased Minimax Probability Machine (BMPM). Different from previous methods, this model directly controls the accuracy of classification of the future data to build up biased classifiers. Hence, it provides a rigorous treatment on imbalanced data. Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate the performance of our proposed framework, in which encouraging and promising experimental results are obtained.