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A critical component of today's commercial search engines is an advertisement platform. The current state-of-the-art of such platforms is primarily based on advanced keyword matching to determine the relevance of advertisements for users' queries. However, such keyword matching techniques suffer from missing user
We have become able to get enough approvable images of a target object just by submitting its object-name to a conventional keyword-based Web image search engine. However, because the search results rarely include its uncommon images, we can often get only its common images and cannot easily get exhaustive knowledge
where our approach is tested on images retrieved from Google keyword based image search engine. The results show that a combination of our approach as a local image descriptor with another global descriptor outperforms other approaches.
comparison features in real time. In addition the img(Rummager) application can execute a hybrid search of images from the application server, combining keyword information and visual similarity. Also img(Rummager) supports easy retrieval evaluation based on the normalized modified retrieval rank (NMRR) and average precision
user aims, as query without proper intent processing retrieves irrelevant information pattern discovery has ability to solve in limitations of keyword and image disambiguates with phrase learning ie, pattern discovery. Today's search machines are based on ranking model eliminating Boolean retrieval constraint and boosting
Users usually search images in different web image search engines by inputting a specified keyword. However, the true images, drawing images, sometimes contemporary images exist in search results. To solve this problem, this paper proposed a simple color feature by using the histogram method for building image
In this paper, we propose a multimodal query suggestion method for video search engine which can leverage multimodal processing to improve the quality of search results. When users type general or ambiguous textual queries, our system provides keyword suggestions and representative image examples in an easy-to-use
This paper presents the image retrieval results obtained by the Flickr website based on using the combination of keywords and tags for retrieving images. The aim of this paper is to improve the accuracy of the image search results returned by an image search engine, Flickr. In order to improve the retrieval
Finding information based on an object's profile is very useful when exact keywords for the object are unknown. Current image retrieval system all ignores the color information, for example we want to find a super-star with a piece of red petticoat, or we want to a red flower with white background. They all cannot
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