基于图理论的图像有哪些信誉好的足球投注网站结果重排序的研究-计算机应用技术专业论文.docx

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