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

  • Sami Abduljalil AbdulhakEmail author
  • Walter Riviera
  • Nicola Zeni
  • Matteo Cristani
  • Roberta Ferrario
  • Marco Cristani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

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.

Keywords

Training set Semantic Ontology Semantic similarity Image retrieval Textual tags Flickr Object recognition 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sami Abduljalil Abdulhak
    • 1
    Email author
  • Walter Riviera
    • 1
  • Nicola Zeni
    • 2
  • Matteo Cristani
    • 1
  • Roberta Ferrario
    • 2
  • Marco Cristani
    • 1
  1. 1.Department of Computer ScienceVeronaItaly
  2. 2.Laboratory for Applied OntologyConsiglio Nazionale Delle Ricerche (CNR)TrentoItaly

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