Texture features for image classification and retrieval

Mohamed Borchani; Georges Stamon

doi:10.1117/12.290360

6 October 1997 Texture features for image classification and retrieval

Mohamed Borchani, Georges Stamon

Proceedings Volume 3229, Multimedia Storage and Archiving Systems II; (1997) https://doi.org/10.1117/12.290360
Event: Voice, Video, and Data Communications, 1997, Dallas, TX, United States

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.

Citation Download Citation

Mohamed Borchani and Georges Stamon "Texture features for image classification and retrieval", Proc. SPIE 3229, Multimedia Storage and Archiving Systems II, (6 October 1997); https://doi.org/10.1117/12.290360

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