Human vision perceptual color based semantic image retrieval with relevance feedback

Bridging the semantic gap between the low level visual features extracted by computers such as color, texture or shape and high level semantic concepts perceived by humans is the main challenge in the aim of increasing the precision of semantic results into Content-Based Image Retrieval (CBIR). This challenge has been approached with the technique known as Relevance Feedback (RF). The technique of RF can be applied through two methods, biased subspace learning or query movement. The method of query movement is based on Rocchio algorithm. In this paper, we present a new optimization to technique of Relevance Feedback through query movement to develop a CBIR system with better semantic precision. We make a modification to the input images color channels composition in the additive color space (Red, Green, Blue) and perceptual additive color space (Hue, Saturation, Value), through the images representation with human photopic vision behavior, which provides the semantic perception of the colors. With the proposed representation we obtained a more accurate behavior of the Color Histogram (CH), Color Coherence Vector (CCV) and Local Binary Patterns (LBP) descriptors in Rocchio algorithm, thus, a query movement oriented more to the semantics of the user. The optimization performance was measured with a subset of 137 classes with 100 images each one from Caltech256 object database. The results show a significant improvement in the semantic precision in comparison to the P. Mane RF method with prominent features, as well as the performance of CBIR systems without RF using the mentioned descriptors.