Leveraging active learning for relevance feedback using an information theoretic diversity measure
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Small-sample learning in image retrieval is a pertinent and interesting problem. Relevance feedback is an active area of research that seeks to find algorithms that are robust with only a small number of examples. Much work has been done in both the machine learning and pattern recognition communities to develop algorithms that learn a high-level semantic concept in a low-level image feature space. In this paper we seek to leverage techniques from both these communities to explore a hybrid relevance feedback system which combines the insight gained from discriminant analysis and active learning. Our technique uses a diversity-based pool-query technique along with biased discriminant analysis to improve the query refinement process. Comparative results are observed and thoughts for future work are presented.