ImprovingWeb-based Image Search via Content Based Clustering

  • Authors:

  • Nadav Ben-Haim;Boris Babenko;Serge Belongie

  • Affiliations:

  • University of California, San Diego;University of California, San Diego;University of California, San Diego

  • Venue:
  • CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
  • Year:
  • 2006

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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.