Precise image retrieval on the web with a clustering and results optimization

Effective image searching in WWW has become important to various users, and the image meta-search engine is an effective technique to improve the quality of retrieval results of Web images on the Internet. The emphasis of the thesis is to propose a model of image meta-search engines, and a vectoriza...

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Bibliographic Details
Published in2007 International Conference on Wavelet Analysis and Pattern Recognition Vol. 1; pp. 188 - 193
Main Authors Heng-Jie Li, Jian-Kun Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2007
Subjects
Algorithm design and analysis
clustering
Clustering methods
Design optimization
genetic algorithm
Genetic algorithms
Image retrieval
Internet
Metasearch
search engine
Search engines
Testing
World Wide Web
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Summary:Effective image searching in WWW has become important to various users, and the image meta-search engine is an effective technique to improve the quality of retrieval results of Web images on the Internet. The emphasis of the thesis is to propose a model of image meta-search engines, and a vectorization method was adopted to apply HACM (hierarchical agglomerative clustering methods) clustering techniques on images search that are then optimized by a specially designed genetic algorithm. The method provides a more significant and restricted set of images as the final result for a user's search on an image meta-search engine. Some experiments have been run on to test the image meta-search engine. The method enables the image meta-search engine to handle a query term in a reasonably short time and return the results with high accuracy.
ISBN:9781424410651
1424410657
ISSN:2158-5695
DOI:10.1109/ICWAPR.2007.4420661