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- W23466914 abstract "One of the main obstacles in capturing semantic of images present in the Web document is that it is difficult to describe the semantics. The high-level textual information of images can be extracted and associated with the textual keywords for narrowing the search space and improving the precision of retrieval. In this paper, we propose a weighted matrix approach for associating keywords and high-level semantics of images to use their complementing strengths. The high-level semantics of the image is described in the HTML documents in the form of image name, optional description and caption. A weighted matrix is constructed using both textual keywords and image information for measuring the association between the keywords and the HTML page. The frequency of occurrence of words can be calculated and used for measuring the relevance of keywords to images. The retrieval performance is compared with various recently proposed techniques and is found to be encouraging." @default.
- W23466914 created "2016-06-24" @default.
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- W23466914 date "2011-01-01" @default.
- W23466914 modified "2023-09-25" @default.
- W23466914 title "Weighted Matrix for Associating High-Level Features with Images in Web Documents for Image Retrieval" @default.
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- W23466914 doi "https://doi.org/10.1007/978-3-642-19263-0_39" @default.
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