A fuzzy statistical correlation-based approach to content-based image retrieval

This paper presents an effective fuzzy long-term semantic learning method for relevance feedback-based image retrieval. The proposed system uses a statistical correlation-based method to dynamically learn the semantic relations between any relevance feedback image pairs. The learned semantic relations are used to automatically expand the feedback set to balance the number of positive and negative images to improve the fuzzy SVM-based low-level learning. They are also used to more accurately estimate the semantic similarity between the query image and database images. The overall similarity score between query and database images is computed by combining both low-level visual and high-level semantic similarity measures. Our extensive experimental results show the proposed system achieves the best retrieval accuracy when compared with three peer systems.