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Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utter importance since human visual system primarily uses them for perception. In this paper we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for...
an important problem in colour content-based image retrieval (CBIR) is the lack of an effective way to represent both the colour and spatial information of an image. In order to solve this problem, a new dominant colour descriptor that employs spatial information of image is proposed. A maximum of three dominant colour regions in an image together with their respective coordinates of the minimum-bounding...
Content-based image retrieval - CBIR uses visual content (low-level features) of images such as color, texture, shape, etc. to representand to index images. Extensive experiments on CBIR show that low-level features not represent exactly the high-level semantic concepts and can fail when used to retrieve similar images. In order to overpass this problem, different approaches aim to propose new methods...
Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. Semantics based methods have been used to describe images according to their high level...
Content-based retrieval of multimedia data has still been an active research area. The efficient retrieval in natural images has been proven a difficult task for content-based image retrieval systems. In this paper, we present a system that adapts two different index structures, namely Slim-Tree and BitMatrix, for efficient retrieval of images based on multidimensional low-level features such as color,...
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