ML Anthology

Prosemantic Image Retrieval

Gianluigi Ciocca, Claudio Cusano, Simone Santini, Raimondo Schettini
ECCV 2012 pp. 643-646
doi:10.1007/978-3-642-33885-4_72 /eccv/2012/ciocca2012eccv-prosemantic/

Abstract

In this technical demonstration we present a content-based image retrieval system based on the ‘query by example’ paradigm. The system effectiveness will be proved for both category and target search on two standard image databases, even without a “good” initial example and ancillary information, such as device metadata, text annotations, etc. These results are obtained by incorporating in the system our recently proposed prosemantic features coupled with a relevance feedback mechanism, and by maximizing novelty and diversity in the result sets.

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Cite

Text

Ciocca et al. "Prosemantic Image Retrieval." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33885-4_72

Markdown

[Ciocca et al. "Prosemantic Image Retrieval." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/ciocca2012eccv-prosemantic/) doi:10.1007/978-3-642-33885-4_72

BibTeX

@inproceedings{ciocca2012eccv-prosemantic,
  title     = {{Prosemantic Image Retrieval}},
  author    = {Ciocca, Gianluigi and Cusano, Claudio and Santini, Simone and Schettini, Raimondo},
  booktitle = {European Conference on Computer Vision},
  year      = {2012},
  pages     = {643-646},
  doi       = {10.1007/978-3-642-33885-4_72},
  url       = {https://mlanthology.org/eccv/2012/ciocca2012eccv-prosemantic/}
}