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In this work we aim to capitalize on the availability of Internet image search engines to automatically create image training sets from user provided queries. This problem is particularly difficult due to the low precision of image search results. Unlike many existing dataset gathering approaches, we do not assume a category model based on a small subset of the noisy data or an ad-hoc validation set...
The keyword-based Google images search engine is now becoming very popular for online image search. Unfortunately, only the text terms that are explicitly or implicitly linked with the images are used for image indexing but the associated text terms may not have exact correspondence with the underlying image semantics
This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can
Keyword-based search exploits the exact match between the index terms of a query and documents. Thus, some documents, although they are relevant to the given query, may not be returned to users unless the documents include the index terms of the query. Some search engines use the authority of documents, which is
The existing search engines are always lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. So through analyzing the dynamic search behavior of users, the paper introduces a new method of using a keyword query graph
The Web represents one of the largest repositories of information ever compiled by mankind and as such search techniques are essential to navigating its depths and returning pertinent information. Typically the search techniques employed in search engines such as Google entail the use of keywords in which Web pages
Current search engine performances need to be improved because often the result suggested by search engine are determine the popularity of a given page for its associated keywords but does not match specific user expectations. Previous researches have indicated that only 20% to 45% of the common search results are
search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result
The syntactic approach of most of Web search engines still has the drawback of not considering the semantics of the keywords entered by the user. So, users usually have to browse many hits looking for the information they want. In this paper, we present a system that, given a set of keywords with well defined
This paper proposed an ontology-support web focused-crawler: OntoCrawler III for Java programs, in which only the user entered some keywords would the system supported by the domain ontology actively provide comparison and verification for those keywords so as to up-rise the precision and recall rates of webpage
A method of multi-text fusion computation is discussed in this paper which extracts the common features automatically by using text fusion. When search the information in a special domain, the keywords are picked out by using relative sample muster fusion, keywords' flexible control is realized by regulating the
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