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.

This paper presents a results fusion approach through multiple representations and multiple queries to tackle the problem of invariance in content-based image retrieval (CBIR). We consider the case of textures. This approach considers invariance at the query level rather than at the representation level. We use two models to capture the textural visual content of images: the autoregressive (AR) model and a perceptual (PCP) model based on a set of perceptual features. The perceptual model is used with two viewpoints: the original images viewpoint and the autocovariance function viewpoint. These representation models are not invariant with respect to geometric and photometric transformations. Then, by using results fusion of multiple representations and/or multiple queries, we show that retrieval effectiveness is improved in an important way even though the representation models are not invariant. The resulting invariant texture retrieval algorithm has two levels of results fusion (merging): 1. The first level consists in merging results returned by the different models / viewpoints (representations) for the same query in one results list using appropriate fusion models; 2. The second level consists in merging each fused list of results of different queries into a unique fused list using appropriate fusion models. Experimental retrieval results are promising.

This paper proposes an interpretation of the estimated parameters of the autoregressive model, used to model texture content of images, that corresponds to human perception. First, we define briefly the AR model and the parameters estimation process. A perceptual interpretation of the AR estimated parameters is then proposed and discussed. In particular, a computational measure to estimate the degree of randomness/regularity of textures is proposed based on the estimated parameters. Experimental results, obtained on the well-known Brodatz database of textures, support the proposed perceptual interpretation.

Texture is a very important image feature extremely used in various image processing problems. It has been shown that humans use some perceptual textural features to distinguish between textured images or regions. Some of the most important features are coarseness, contrast, direction and busyness. In this paper a new method based on the autocovariance function to estimate quantitatively these features is shown and the correspondence between these computational measures and the psychological ones made by human subjects is shown using some psychometric method.

It has been shown that humans use some perceptual textural features such as coarseness, contrast and direction to distinguish between textured images or regions. The aim of this paper is to present a new method to estimate these perceptual textural features using the autocovariance function. Computational measures derived from the autocovariance function to estimate these perceptual textural features are presented. Experimental results are then given and the correspondence between the computational measures proposed and the psychological measures is shown using some psychometric method.