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approach has a limit as only the annotations of found images during the interaction are updated. In this paper we introduce a novel method of semi-automatic annotation. The method is using visual feature representations of keywords which are improved during the region-based relevance feedback. The experiments show that this
Content-based image retrieval (CBIR) has been adopted as a complementary technique to the keyword-based image search. Relevance feedback (RFB) is considered as an effective means to bridge the gap between the designated features and the run-time semantics on a CBIR system. Like many other interactive system, a good
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
The content based image retrieval (CBIR) is one of the most popular, rising research areas of the digital image processing. Most of the available image search tools, such as Google Images and Yahoo! Image search, are based on textual annotation of images. In these tools, images are manually annotated with keywords and
degree of relevancy for the user than is currently available with conventional methods, for example, using matching keywords. We describe here our method and the relation between the scenes and discuss a prototype system.
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