Relevance Feedback and Category Search in Image Databases

We present a sound framework for relevance feedback in content-based image retrieval. The modeling is based on non-parametric density estimation of relevant and non-relevant items and Bayesian inference. This theory has been successfully applied to benchmark image databases, quantitatively demonstrating its performance for target search, selective control of precision and recall in category search, and improvement of retrieval effectiveness. The paper is illustrated with several experiments and retrieval results on real-world data.