Computational perceptual features for texture representation and retrieval

A perception-based approach to content-based image representation and retrieval is proposed in this paper. 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 new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, and busyness. The proposed computational measures can be based upon two representations: the original images representation and the autocorrelation function (associated with original images) representation. The set of computational measures proposed is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results and benchmarking show interesting performance of our approach. First, the correspondence of the proposed computational measures to human judgments is shown using a psychometric method based upon the Spearman rank-correlation coefficient. Second, the application of the proposed computational measures in texture retrieval shows interesting results, especially when using results fusion returned by each of the two representations. Comparison is also given with related works and show excellent performance of our approach compared to related approaches on both sides: correspondence of the proposed computational measures with human judgments as well as the retrieval effectiveness.