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. First, the related textual information associated with Web images is identified as the candidate annotations for Web images. Second, the word co-occurrence is utilized to eliminate irrelevant keywords for improving the annotation accuracy. Then, the keyword-based association analysis is exploited to further discover
More and more abundant Web images on the Internet make clients difficult seek the information they really need so that how to quickly and accurately retrieve their interested Web images is one of the most challenging tasks. The kernel idea of the model is that the text keyword features, visual content features, link
With the large number of Web sites promoting the use of illicit drugs, it has become important to screen these sites for the protection of children on the Internet. Conventional keyword-based approaches are not sufficient because these Web sites often have lots of images and little meaningful words than prices. We
In this paper, we propose a new method to select relevant images to the given keywords from the images gathered from the Web. Our novel method is based on the probabilistic latent semantic analysis (PLSA) model, which is a generative probabilistic topic model. Firstly, we gather images related to the given keywords
performance improvement. Third, a comprehensive method of annotation refinement is developed to remove the noisy keywords. Finally, experimental results demonstrate the effectiveness of our proposed system.
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