Classification and retrieval of images using texture features

The panorama of the paper is content based image representation and retrieval. The major key flavor is texture content by a set of features having a perceptual meaning. We consider textured images and propose to model their textural content by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, busyness and periodicity (textons). The proposed computational measures can be based upon the autocorrelation function (associated with original images) representation. The set of computational measures proposed is applied to content-based image retrieval on fundus image data set, the well-known Drive and Stare database. The invisible Comparison is given with statistical and structural methods with probing analysis.