3 research outputs found
An Information Theoretic Approach to Content Based Image Retrieval.
We propose an information theoretic approach to the representation and comparison of color features in digital images to handle various problems in the area of content-based image retrieval. The interpretation of color histograms as joint probability density functions enables the use of a wide range of concepts from information theory to be considered in the extraction of color features from images and the computation of similarity between pairs of images. The entropy of an image is a measure of the randomness of the color distribution in an image. Rather than replacing color histograms as an image representation, we demonstrate that image entropy can be used to augment color histograms for more efficient image retrieval. We propose an indexing algorithm in which image entropy is used to drastically reduce the search space for color histogram computations. Our experimental tests applied to an image database with 10,000 images suggest that the image entropy-based indexing algorithm is scalable for image retrieval of large image databases. We also proposed a new similarity measure called the maximum relative entropy measure for comparing image feature vectors that represent probability density functions. This measure is an improvement of the Kullback-Leibler number in that it is non-negative and satisfies the identity and symmetry axioms. We also propose a new usability paradigm called Query By Example Sets (QBES) that allows users, particularly novice users, the ability to express queries in terms of multiple images
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Perceived features and similarity of images: An investigation into their relationships and a test of Tversky's contrast model.
The creation, storage, manipulation, and transmission of images have become less costly and more efficient. Consequently, the numbers of images and their users are growing rapidly. This poses challenges to those who organize and provide access to them. One of these challenges is similarity matching. Most current content-based image retrieval (CBIR) systems which can extract only low-level visual features such as color, shape, and texture, use similarity measures based on geometric models of similarity. However, most human similarity judgment data violate the metric axioms of these models. Tversky's (1977) contrast model, which defines similarity as a feature contrast task and equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, explains human similarity judgments much better than the geometric models. This study tested the contrast model as a conceptual framework to investigate the nature of the relationships between features and similarity of images as perceived by human judges. Data were collected from 150 participants who performed two tasks: an image description and a similarity judgment task. Qualitative methods (content analysis) and quantitative (correlational) methods were used to seek answers to four research questions related to the relationships between common and distinctive features and similarity judgments of images as well as measures of their common and distinctive features. Structural equation modeling, correlation analysis, and regression analysis confirmed the relationships between perceived features and similarity of objects hypothesized by Tversky (1977). Tversky's (1977) contrast model based upon a combination of two methods for measuring common and distinctive features, and two methods for measuring similarity produced statistically significant structural coefficients between the independent latent variables (common and distinctive features) and the dependent latent variable (similarity). This model fit the data well for a sample of 30 (435 pairs of) images and 150 participants (χ2 =16.97, df=10, p = .07508, RMSEA= .040, SRMR= .0205, GFI= .990, AGFI= .965). The goodness of fit indices showed the model did not significantly deviate from the actual sample data. This study is the first to test the contrast model in the context of information representation and retrieval. Results of the study are hoped to provide the foundations for future research that will attempt to further test the contrast model and assist designers of image organization and retrieval systems by pointing toward alternative document representations and similarity measures that more closely match human similarity judgments