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Automatic image annotation is a promising methodology for image retrieval. However most current annotation models are not yet sophisticated enough to produce high quality annotations. Given an image, some irrelevant keywords to image contents are produced, which are a primary obstacle to getting high-quality image
Image annotation is usually formed as a multiclass classification problem. Traditional methods learn the co-occurrence of keywords and images while they ignore the correlation between keywords, which turned out to be one of the reasons causing poor experiment results. In this paper, we propose an automatic image
Semantic image retrieval using text such keywords or captions at different semantic levels has attracted considerable research attention in recent years. Automatic image annotation (AIA) has been proved to be an effective and promising solution to automatically deduce the high-level semantics from low-level visual
containing a candidate sentence is computed as the cosine of the angle between the question keywords vector and the document vector. Since the semantic feature is more reliable on content verbs and syntactic similarity is suitable for questions with a subject- verb-object syntactic structure, we only consider questions with a
algorithms, web image information is extracted from textual sources such as image file names, anchor texts, existing keywords and, of course, surrounding text. However, the systems that attempt to mine information for images using surrounding text suffer from several problems, such as the inability to correctly assign all
Web page recommendation model traces userspsila Web-surfing trails, extracts the useful information including keywords, Web page URLs and userspsila evaluations on Web pages, and automatically generates FCA (formal concept analysis) knowledge base and enterprise ontology knowledge base with WordNet. While users are
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