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Access Type
Open Access
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2012
Month Degree Awarded
September
Keywords
search engine, image retrieval, random walk, book indexing, large scale, cloud computing
Abstract
Search engines play a very important role in daily life. As multimedia product becomes more and more popular, people have developed search engines for images and videos. In the first part of this thesis, I propose a prototype of a book image search engine. I discuss tag representation for the book images, as well as the way to apply the probabilistic model to generate image tags. Then I propose the random walk refinement method using tag similarity graph. The image search system is built on the Galago search engine developed in UMASS CIIR lab.
Consider the large amount of data the search engines need to process, I bring in cloud environment for the large-scale distributed computing in the second part of this thesis. I discuss two models, one is the MapReduce model, which is currently one of the most popular technologies in the IT industry, and the other one is the Maiter model. The asynchronous accumulative update mechanism of Maiter model is a great fit for the random walk refinement process, which takes up 84% of the entire run time, and it accelerates the refinement process by 46 times.
DOI
https://doi.org/10.7275/3273265
First Advisor
James Allan
Second Advisor
Lixin Gao