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used to evaluate these computational measures with respect to human measures is depicted and experimental results are given. It should be noted that the study reported here was tested on a sample of 12 images ( Fig. 1) from Brodatz database [4]. This sample of images has been chosen to be largely representative.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.

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.

Abstract-Academic accreditation of degree programs is becoming an important mean for many institutions to improve the quality of their degree programs. Many programs, in particular computing and engineering, offered by many schools have engaged in the accreditation process with different accreditation bodies. Accreditation bodies include ABET in USA, ABEEK in South Korea, JABEE in Japan, etc. Probably the most known accreditation body in the Unites States of America for engineering, computing, technology, and applied science programs is ABET. A key problem towards the satisfaction of accreditation criteria for most of accreditation agencies including ABET is the appropriate definition and assessment of program educational objectives for a specific degree program. Program Educational Objectives are important as they represent the ultimate mean to judge the quality of a program. They related directly to student outcomes and curriculum of a degree program. We propose a set of guidelines to help understand how program educational objectives can be defined and assessed. We relate and use examples from our practical experience acquired while working on the ABET accreditation of a Software Engineering program.

This paper addresses the fundamental issues of visual content representation and similarity matching in content-based image retrieval and image databases in general. Simply stated, defining an image retrieval system is equivalent to find answers to two fundamental questions: 1. Representation model or which features are used to represent the content of images; 2. Once the set of features representing the content of images is determined, the question of how to combine the individual or partial similarities according to each feature to form a global similarity must be addressed. In this paper, a new similarity model is introduced based on the Gower coefficient of similarity. This similarity model is flexible and can be declined in several versions: nonweighted, weighted and hierarchical versions. This model was applied to a sample of homogeneous textured images considering two representation models: the autoregressive model, a purely statistical model, and an empirical perceptual model based on perceptual features such as coarseness and directionality. Experimentations show very interesting results.