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AbstractAbstract
Abstract
Abstract
With the development of the search engine,users have been accustomed to search all kinds of information,including text,images and videos on the Internet with the help of various types of search engines.Traditional text—based image retrieval
system,most of which rely on key words search.It is the text information contains a
lot of noise.this will lead to the search results are not ideal.Existing mainstream Intemet search engines,such as Google,Bing,Baidu etc..Most of these search
engines use the text information around the image to achieve the image search and
rank.These search engines lack the intrinsic link between the image and the contents of the image itself,which leads to the unsatisfactory results of the text-based image search results.Now the researchers focus on how to improve the quality of image
search results.
Image reranking is based on the initial search results.We can extract the inner
link of the image and the contents of the image itself.Image reranking is to rank the initial results,then the final reranking results will be the user needs.At present, according to the different framework,the image reranking methods can be classified into four categories:based on linear combination,based on clustering,based on classification and based on graph.The existing graph-based image reranking methods generally used pseudo relevance methods for initial ranking results.These methods consider the front images to have high scores.But it is not a truth.As the initial ranking results are based on the text retrieval,the accuracy of the ranking results is low.It may cause the back of the image to be the user’S need.The contribution of this work iS summarized as follows:
1.To improve the effectiveness of reranking algorithm for image retrieval,this paper presents a multimodal graph-based reranking through random walk
algorithm.Firstly,different from the existing reranking algorithms that initialize
the relevance score list of the retri
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