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- W2020175018 abstract "ABSTRACT Increased interest in content-based storage and retrieval of images and video frames has been stemmed from its potential applications in multimedia information systems. Various matching methods have been proposed in the literature, including histogram intersection, distance method, and reference table method.A comparison of these three techniques has proved that the reference table method is the best in terms ofretrieval efficiency. However, the drawback of this method is that it requires a pre-defined set of referencefeature (color, in particular) which can approximately cover all features (colors) in the selected application. While this condition may be satisfied in some applications, in situations where there are continuing additions and/or deletions to the database and where knowledge of features in the images is not available apriori, such a technique will not produce very reliable results. The reference feature or color table methodrequires a representative sample of all images stored in the database in order to select the reference featureor color table. For example, such a priori knowledge is impossible to obtain in a trade-marks database. Toalleviate the reference table requirement, recent works suggest the use of unsupervised feature matchingbased on color-clustering, which is a computationally expensive approach. In this study, we propose animage retrieval method based on the relative entropy (Eei), kflOWfl as the Kuilback directed divergence.This measure is nonnegative and it is zero if and only if two distributions are identical; i.e., perfect match.Erei has only one minimum for every comparison. This offers a unique criterion for optimization with lowcomputational complexity. It also provides a thoughtful view for the type of data distribution in the sensethat the whole range of data distribution is considered in matching and not only some moments.The algorithm described here has been tested on an imaging database system, consisting of 100 variousimages of different object and texture scenes stored in a content addressable stack. The efficacy of retrievalis presented by listing the retrieval results using different query images. The experimental results show thatthe relative entropy is effective for ordering the images of a database system in accordance with thesimilarity between their gray-level distributions.Keywords: Relative entropy, image histograms, database retrieval, query image" @default.
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- W2020175018 modified "2023-09-24" @default.
- W2020175018 title "<title>Relative entropy-based feature matching for image retrieval</title>" @default.
- W2020175018 doi "https://doi.org/10.1117/12.373478" @default.
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