ML Anthology

Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags

Sami Abduljalil Abdulhak, Walter Riviera, Nicola Zeni, Matteo Cristani, Roberta Ferrario, Marco Cristani
ECCVW 2014 pp. 309-322
doi:10.1007/978-3-319-16181-5_22 /eccvw/2014/abdulhak2014eccvw-semanticanalysis/

Abstract

Training sets of images for object recognition are the pillars on which classifiers base their performances. We have built a framework to support the entire process of image and textual retrieval from search engines, which, giving an input keyword, performs a statistical and a semantic analysis and automatically builds a training set. We have focused our attention on textual information and we have explored, with several experiments, three different approaches to automatically discriminate between positive and negative images: keyword position, tag frequency and semantic analysis. We present the best results for each approach.

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Cite

Text

Abdulhak et al. "Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags." European Conference on Computer Vision Workshops, 2014. doi:10.1007/978-3-319-16181-5_22

Markdown

[Abdulhak et al. "Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags." European Conference on Computer Vision Workshops, 2014.](https://mlanthology.org/eccvw/2014/abdulhak2014eccvw-semanticanalysis/) doi:10.1007/978-3-319-16181-5_22

BibTeX

@inproceedings{abdulhak2014eccvw-semanticanalysis,
  title     = {{Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags}},
  author    = {Abdulhak, Sami Abduljalil and Riviera, Walter and Zeni, Nicola and Cristani, Matteo and Ferrario, Roberta and Cristani, Marco},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2014},
  pages     = {309-322},
  doi       = {10.1007/978-3-319-16181-5_22},
  url       = {https://mlanthology.org/eccvw/2014/abdulhak2014eccvw-semanticanalysis/}
}