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This paper discusses techniques for improving the performance of keyword-based web image queries. Firstly, a web page is segmented into several text blocks based on semantic cohesion. The text blocks which contain web images are taken as the associated texts of corresponding images and TF*IDF model is
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
FCA, a session interest concept is defined as a pair of extent and intent where the extent covers a set of documents selected by the user among the search results and the intent covers a set of keyword features extracted from the selected documents. And, in order to make a concept network grow, we need to calculate the
A simple search keyword usually returns million of search results. The result count may appear impressive, at the same time it confuse the users. User usually will not wish to browse through million of entries. This paper proposed a query refinement method by iterative clustering of information from the Web page
Most researches on Image Retrieval (IR) have aimed at clearing away noisy images and allowing users to search only acceptable images for a target object specified by its object-name. We have become able to get enough acceptable images of a target object just by submitting its object-name to a conventional keyword
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