Improving web-based image search via content based clustering
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
ISSN | 1063-6919 |
DOIs | |
Publication status | Published - 2006 |
Externally published | Yes |
Event | 2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States Duration: 17 Jun 2006 → 22 Jun 2006 |
Conference
Conference | 2006 Conference on Computer Vision and Pattern Recognition Workshops |
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Country | United States |
City | New York, NY |
Period | 17/06/2006 → 22/06/2006 |
ID: 302053942