Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval by Relevance Feedback
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
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
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
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 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.