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In this paper, an adaptive recognition model (ARM) is proposed for image annotation. The ARM consists of an adaptive classification network (CFN) and a nonlinear correlation network (CLN). The adaptive CFN aims to annotate an image with keywords, and the CLN is used to unveil the correlative information of keywords
browsing can identify an image. In text-based retrieval, images are retrieved using keywords, like subject, headings, or classification codes, which in turn are used as retrieval keys during search and retrieval of images. Usually, the only way of searching these collections of images was by keyword indexing, or simply by
using feature vector. We do static analysis over computed features to get distinguishing feature descriptors. Maximum similarity i.e. minimum distance allows us to find the query relevant combined pictures and associated relevant words. For textual part of the query we compute the concepts (keywords as well as synonyms of
Despite the tremendous importance and availability of large video collections, support for video retrieval is still rather limited and is mostly tailored to very concrete use cases and collections. In image retrieval, for instance, standard keyword search on the basis of manual annotations and content-based image
have resolved most difficulties when a user is able to provide appropriate keywords of his/her search target. Nevertheless, some important events which are etched deeply in one's memory may not be clearly defined as a few keywords or even easily recalled. Thus, we propose in this work to provide some visual suggestions to
system considering artifacts using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only color information but also spectrum and keywords are employed. Moreover, the original image is divided into some
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