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This research is concerned with the table based KNN as the approach to the keyword extraction task. The keyword extraction task is viewed as an instance of word classification, and it is discovered that encoding words into tables improved the word categorization performance. In this research, words are encoded into
Keyword-based image search engines like Google Images are now very popular for getting large amount of images on the web. Because only the text information that are directly or indirectly linked to the images are used for image indexing and retrieval, most existing image search engines such as Google Images may return
Topic tracking is to track trend of news topic, which people are interested in. It is a very pragmatic method in information retrieval. Compared with keywords retrieval, topic tracking excels in dynamic tracking based on text model and its content understanding, so it is mostly involved in text expressing and semantic
associated with intermediate semantic descriptors. The intermediate descriptors are used also for image categorization and for qualitative definition of semantic keywords in the user queries. For improving the initial query results, we apply a relevance feedback mechanism that uses the low -level descriptors of the images
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
index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC). For this purpose, we developed fully automated methods for mapping keywords to their corresponding ontology concepts. Support vector machine a successful machine learning technique is used for classification. Experimental results shows that
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