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The main focus of this paper is to present a methodology for optimizing relevance identification in content-based image retrieval (CBIR) systems through the principle of feature weight detection. The purpose of relevance identification is to find a collection of images that are statistically similar to, or match with, an original query image within a large visual database. The novelty of this scheme...
In this work, we present a modified version of the generic Fourier descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduced the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise...
The overall objective of this paper is to present a methodology for guiding adaptations of an RBF based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The self organizing tree map (SOTM) is essentially attractive for our approach since it not only extracts global intuition from an input...
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