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PageRank algorithm in the problem of non-web search

Author: ZhaoBo
Tutor: TaoXiaoPeng
School: Fudan University
Course: Computer Software and Theory
Keywords: PageRank Image Retrieval Performance Evaluation
CLC: TP391.3
Type: Master's thesis
Year: 2010
Downloads: 121
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Abstract


In 1998, many important theories are proposed on web information retrieval. Two of them are HITS (Hypertext Induced Topic Search) [5] and PageRank [11]. Because its inherent impervious to spam web pages and its query independent, PageRank become the dominant web information retrieval method. PageRank’s strength is also HITS’s shortage.With the great success of the computer hardware industry, more and more multimedia documents are available on the web. This proposes new requirements for search engines. Different from the well researched web page retrieval technique, multimedia web information retrieval did not have effective method for a long time. The traditional way is to use the text-based method to retrieve the text information around the images, etc. Recently, researchers tried to exploit the multimedia information itself to improve the retrieval result. Fergus et al[1] used a learning-based method. They collect top images returned by Google Image Search engine and used its visual information to re-rank all the collected images. Jing et al[2,3,4] expand this work to fit the web image retrieval work. They also collected the top returned results of Google Image Search engine and extract the local image features. The different is they used locality-sensitive hashing [15] to match the features for all the images. This result a similarity graph of the image set. Then have this graph they used PageRank to re-rank all the images. Their experiments show big improvement to the current image search systems. VisualRank also have its problems, one is it will do the match for all the features regardless the image set quality and the other is locality-sensitive hashing may require too many memory. To handle these two problems, we proposed image retrieval method based on silent local features.PageRank is a method to use the inherent similarity information of a data set to compute its important factors. So it may be used in many more fields. The GPU architecture becomes more and more complex and different models have tight connections with each other. We also proposed PageRank-based method to evaluate GPU performance.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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