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In Web 2.0 applications, users always label digital images using textual descriptions, which are also called tags. As a result, a web image usually carries both tag and visual content information. In order to improve the retrieval performance of web images, in this paper, we propose an error-driven fusion co-clustering algorithm, which combines images' tags, visual contents together for analysis....
An approach to identification of the phishing target of a given (suspicious) webpage is proposed by clustering the webpage set consisting of its all associated webpages and the given webpage itself. We first find its associated webpages, and then explore their relationships to the given webpage as their features for clustering. Such relationships include link relationship, ranking relationship, text...
This paper compares the efficacy and efficiency of different clustering approaches for selecting a set of exemplar images, to present in the context of a semantic concept. We evaluate these approaches using 900 diverse queries, each associated with 1000 web images, and comparing the exemplars chosen by clustering to the top 20 images for that search term. Our results suggest that Affinity Propagation...
Keeping track of news stories and events as they progress can be a tedious job, but as every day routine most of the web users read and follow many stories and events in news. If an analyst in her area has to follow and map all these according to the time-line they happen, the task quickly becomes overwhelming. We present an online tool which attempts to ease the analyst's task of finding all news...
This paper proposes an efficient approach to find clusters of spatially related scene images collected from the website. Our method firstly builds a guide table, in which the ranked results are given according to the relevance scores of image pairs obtained by the image retrieval methods. Then the image clusters are generated by repeatedly choosing a seed image and performing query expansion directed...
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