Improving web-based image search via content based clustering

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Improving web-based image search via content based clustering. / Ben-Haim, Nadav; Babenko, Boris; Belongie, Serge.

In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Ben-Haim, N, Babenko, B & Belongie, S 2006, 'Improving web-based image search via content based clustering', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPRW.2006.100

APA

Ben-Haim, N., Babenko, B., & Belongie, S. (2006). Improving web-based image search via content based clustering. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPRW.2006.100

Vancouver

Ben-Haim N, Babenko B, Belongie S. Improving web-based image search via content based clustering. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006. https://doi.org/10.1109/CVPRW.2006.100

Author

Ben-Haim, Nadav ; Babenko, Boris ; Belongie, Serge. / Improving web-based image search via content based clustering. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006.

Bibtex

@inproceedings{4fa24ecea253414db6906ba36cc41f02,
title = "Improving web-based image search via content based clustering",
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.",
author = "Nadav Ben-Haim and Boris Babenko and Serge Belongie",
year = "2006",
doi = "10.1109/CVPRW.2006.100",
language = "English",
journal = "I E E E Conference on Computer Vision and Pattern Recognition. Proceedings",
issn = "1063-6919",
publisher = "Institute of Electrical and Electronics Engineers",
note = "2006 Conference on Computer Vision and Pattern Recognition Workshops ; Conference date: 17-06-2006 Through 22-06-2006",

}

RIS

TY - GEN

T1 - Improving web-based image search via content based clustering

AU - Ben-Haim, Nadav

AU - Babenko, Boris

AU - Belongie, Serge

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33845516856&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2006.100

DO - 10.1109/CVPRW.2006.100

M3 - Conference article

AN - SCOPUS:33845516856

JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

SN - 1063-6919

T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops

Y2 - 17 June 2006 through 22 June 2006

ER -

ID: 302053942