<title>Texture features for image classification and retrieval</title>

Abstract

In this paper, we present an approach to texture-based image retrieval using image similarity on the basis of the matching of selected texture features. Image texture features are generated via gray level co-occurrence matrix, run-length matrix, and image histogram. Since they are computed over gray levels, color images of the database are first converted to 256 gray levels. For each image of the database, a set of texture features is extracted. They are derived from a modified form of the gray level co-occurrence matrix over several angles and distances, from a modified form of the run-length matrix over several angles, and from the image histogram. A sequential forward search is performed on all these features to reduce the dimensionality of the feature space. A supervised classifier is then applied to this reduced feature space in order to classify images into well separated classes. For measuring the similarity between two images a distance between two texture feature vectors is calculated. First experiments with multiple queries in a large image database give good results in terms of both speed and classification rate.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.