Similarity learning for texture image retrieval using multiple classifier system

Multiple Classifier System has found its applications in many areas such as handwriting recognition, speaker recognition, medical diagnosis, fingerprint recognition, personal identification and others. However, there have been rare attempts to develop content-based image retrieval (CBIR) system that uses multiple classifiers to learn visual similarity. Texture as a primitive visual content is often used in many important applications (viz. Medical image analysis and medical CBIR system). In this paper, a texture image retrieval system is developed that learns the visual similarity in terms of class membership using multiple classifiers. The way proposed approach combines the decisions of multiple classifiers to obtain final class memberships of query for each of the output classes is also a novel concept. A modified distance that is weighted with the membership values obtained through similarity learning is used for ranking. Three different algorithms are proposed for the retrieval of images against a query image displaying the strength of multiple classifier approach, class membership score and their interplay to achieve the objective defined in terms of simplicity, retrieval effectiveness and speed. The proposed methods based on multiple classifiers achieve higher retrieval accuracy with lower standard deviation compared to all the competing methods irrespective of the texture database and feature set used. The multiple classifier retrieval schemes proposed here is tested for texture image retrieval. However, these can be used for any other challenging retrieval problems.