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Content-based image retrieval (CBIR) systems experience the challenge of semantic gap between the low-level visual features and the high-level semantic concepts. It would be advantageous to build CBIR systems which support high-level semantic query. The main idea is to integrate the strengths of content- and keyword
photographs show that our approach gives better results in terms of recall and precision measures than state-of-the-art frameworks loosely coupling keyword-based query modules and relevance feedback processes operating on low-level features
For automatic image annotation, a method based on rough sets with visual keys is proposed. Using rough set theory the method constructs decision rules about each visual key used for image indexing and about keywords from training set of already annotated images. Then target image is annotated according to constructed
Nowadays semantic image annotation is becoming more than ever a very challenging issue since it helps improving image interpretation and retrieval. Currently, most semantic annotation methods represent images as lists of keywords or histogram of visual words, and do not consider the spatial distribution of regions
sets of keywords is used and a dynamic approach is presented for updating the semantic network and semantic contents of the images in an interactive way. The proposed approach, certainly, executes a novel kind of long term learning-based on user's opinion in the several interactions with system, and relates the behavior
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