Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/424537
Title: Improving web-based image search via content based clustering
Authors: Ben-Haim, Nadav;Babenko, Boris;Belongie, Serge
subject: Improving Web-Based Image Search via Content Based Clustering
Year: 2008
Abstract: Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance. © 2006 IEEE.
Description: 

URI: http://localhost/handle/Hannan/362054
http://localhost/handle/Hannan/424537
Appears in Collections:2002-2008

Files in This Item:
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AL501384.pdf670.04 kBAdobe PDF
Title: Improving web-based image search via content based clustering
Authors: Ben-Haim, Nadav;Babenko, Boris;Belongie, Serge
subject: Improving Web-Based Image Search via Content Based Clustering
Year: 2008
Abstract: Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance. © 2006 IEEE.
Description: 

URI: http://localhost/handle/Hannan/362054
http://localhost/handle/Hannan/424537
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL501384.pdf670.04 kBAdobe PDF
Title: Improving web-based image search via content based clustering
Authors: Ben-Haim, Nadav;Babenko, Boris;Belongie, Serge
subject: Improving Web-Based Image Search via Content Based Clustering
Year: 2008
Abstract: Current image search engines on the web rely purely on the keywords around the images and the filenames, which produces a lot of garbage in the search results. Alternatively, there exist methods for content based image retrieval that require a user to submit a query image, and return images that are similar in content. We propose a novel approach named ReSPEC (Re-ranking Sets of Pictures by Exploiting Consistency), that is a hybrid of the two methods. Our algorithm first retrieves the results of a keyword query from an existing image search engine, clusters the results based on extracted image features, and returns the cluster that is inferred to be the most relevant to the search query. Furthermore, it ranks the remaining results in order of relevance. © 2006 IEEE.
Description: 

URI: http://localhost/handle/Hannan/362054
http://localhost/handle/Hannan/424537
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL501384.pdf670.04 kBAdobe PDF