Use of texture features for image classification and retrieval

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-occurence 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-occurence 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 (one - from the training set, another - from the experimental set) 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.