Content Based Image Retrieval Using Localized Multi-texton Histogram

This paper presents a simple yet efficient image retrieval technique that defines image feature descriptors using localized multi-texton histogram. The proposed technique extracts a unique feature vector for each image in the image database based on its shape, texture and color. First, the image is divided into smaller equal size blocks and then for each block texture orientation is computed independently. Second, each block is filtered with a set of predefined textons and finally, a co-occurrence relation is established from the orientation and the filtered text on image. This relationship in turn provides a powerful feature vector. To retrieve similar images, the feature vector of the query image is computed and compared with the feature vectors of the stored images using Euclidean distance measure. The proposed algorithm is tested on standard image dataset Corel 1000 for accuracy and recall with favorable results. It is also compared with existing state of the art Context Based Image Retrieval algorithm and showed convincing results.