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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
Many social image search engines are based on keyword/tag matching. This is because tag-based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. Recent studies have shown that manual tags are often unreliable and
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
The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a
With the development of computer-aided education and digital library, there have emerged large numbers of digital documents online for education purposes. However, it is far from convenient to retrieve mathematic geometry questions because current retrieval systems largely rely on keywords instead of geometry figure
mechanism to create a codebook with low network cost. Since the number of features in each image is large, compared to a text query generally consisting of several keywords, information exchange between nodes for each query image generates high network cost. In order to further reduce the network cost, we implement two static
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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