Content-based texture image retrieval using fuzzy class membership

Abstract

There is no single best representation of images that can separate different classes with well defined boundaries in the feature space. Therefore, content-based image retrieval (CBIR) using conventional distance metric is not efficient in the low level image feature space viz. texture. Classifier based retrieval approaches (classification followed by retrieval) classify the query image and retrieve images only from the identified class. The performance of such approaches greatly relies on the performance of classifier. For each correct classification of query image, these systems yield high retrieval accuracy and for each misclassification the systems result in complete failure. It results huge variance in performance. This paper proposes a novel approach to content-based image retrieval called “Class Membership-based Retrieval” that addresses the limitations of both conventional distance based and conventional classifier based retrieval approaches. The proposed method consists of two steps. First, the class label and fuzzy class membership of query image is computed using neural network. In the second step, the retrieval is performed using a combination of simple and weighted (class membership based) distance metric in complete search space unlike the conventional classifier based retrieval techniques. The proposed technique also provides flexibility in reducing the search space in steps increasing the speed of retrieval at the cost of gradual reduction in accuracy. The performance of the method is evaluated using three texture data sets varying in orientations, complexity and number of classes. Experimental results support the proposed technique favorably when compared with other promising texture retrieval schemes.