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This paper presents a condensed semantic tree model for representing image category. For a specific application area, a semantic concept space is defined. According to the annotation for an image, a real-value semantic vector is gained that describes the content of it. In order to represent image category, condensed semantic tree model is introduced. It is a triple level structure. The bottom level...
This paper proposes an image retrieval method based on multi-feature similarity score fusion using genetic algorithm. Single feature describes image content only from one point of view, which has a certain one-sided. Fusing multi-feature similarity score is expected to improve the system's retrieval performance. In this paper, the retrieval results from color feature and texture feature are analyzed,...
Content-based image retrieval represents images as N-dimensional feature vectors. Similar image retrieval is computed over these high dimensional feature vectors. A sequential scan of the feature vectors for a query method is costly for a large number of images when N is high. The search time and search space can be reduced through indexing the data. In this paper we proposed a hierarchical clustering...
How to find the image we need expediently in the tremendous database is one of the most important issues in content-based image retrieval (CBIR). In this paper, we presented an image retrieval system based on image content using fuzzy logic and proposed a new concept on partition the entire database based on content self-organized. In detail, we used modified fuzzy c-means (MFCM) clustering scheme...
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