Nadav Ben-Haim

University of California, San Diego, San Diego, CA, United States

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Publications (2)0 Total impact

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    N. Ben-Haim, B. Babenko, S. Belongie
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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.
    Computer Vision and Pattern Recognition Workshop, 2006 Conference on; 07/2006
  • Nadav Ben-Haim, Boris Babenko
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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 which return images that are similar to the query image. We will propose a novel approach that will be a hybrid of the two. We develop a method which first retrieves the results of a keyword query from an existing image search engine, clusters the results based on various extracted features, and returns the cluster that is inferred to be the most relevant to the search query.

Publication Stats

27 Citations

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Institutions

  • 2006
    • University of California, San Diego
      • Department of Computer Science and Engineering (CSE)
      San Diego, CA, United States