ML Anthology

ImprovingWeb-Based Image Search via Content Based Clustering

Nadav Ben-Haim, Boris Babenko, Serge J. Belongie
CVPRW 2006 pp. 106
doi:10.1109/CVPRW.2006.100 /cvprw/2006/benhaim2006cvprw-improvingwebbased/

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. 1.

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Cite

Text

Ben-Haim et al. "ImprovingWeb-Based Image Search via Content Based Clustering." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.100

Markdown

[Ben-Haim et al. "ImprovingWeb-Based Image Search via Content Based Clustering." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/benhaim2006cvprw-improvingwebbased/) doi:10.1109/CVPRW.2006.100

BibTeX

@inproceedings{benhaim2006cvprw-improvingwebbased,
  title     = {{ImprovingWeb-Based Image Search via Content Based Clustering}},
  author    = {Ben-Haim, Nadav and Babenko, Boris and Belongie, Serge J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {106},
  doi       = {10.1109/CVPRW.2006.100},
  url       = {https://mlanthology.org/cvprw/2006/benhaim2006cvprw-improvingwebbased/}
}