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.
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 will define briefly the AR model and the parameters estimation process. A perceptual interpretation of the AR estimated parameters will be 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.
In this paper, we show how the use of multiple content representations and their fusion can improve the performance of content-based image retrieval systems. We consider the case of texture and propose a new algorithm for texture retrieval based on multiple representations and their results fusion. Texture content is modeled using two different models: the well-known autoregressive model and a perceptual model based on perceptual features such as coarseness and directionality. In the case of the perceptual model, two viewpoints are considered: perceptual features are computed based on the original images viewpoint and on the autocovariance function viewpoint (corresponding to original images). So we consider a total of three content representations. The similarity measure used is based on Gower's index of similarity. Simple results of the fusion models are used to merge search results returned by different representations. Experimentations and benchmarking carried out on the well-known Brodatz database show a drastic improvement in search effectiveness with the fused model without necessarily altering their efficiency in an important way.
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