Exploring the Semantics behind a Collection to Improve Automated Image Annotation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Ainhoa Llorente , Enrico Motta , Stefan Rüger

ABSTRACT

The goal of this research is to explore several semantic relatedness measures that help to refine annotations generated by a baseline non-parametric density estimation algorithm. Thus, we analyse the benefits of performing a statistical correlation using the training set or using the World Wide Web versus approaches based on a thesaurus like WordNet or Wikipedia (considered as a hyperlink structure). Experiments are carried out using the dataset provided by the 2009 edition of the ImageCLEF competition, a subset of the MIR-Flickr 25k collection. Best results correspond to approaches based on statistical correlation as they do not depend on a prior disambiguation phase like WordNet and Wikipedia. Further work needs to be done to assess whether proper disambiguation schemas might improve their performance. More... »

PAGES

307-314

References to SciGraph publications

  • 2010. Overview of the CLEF 2009 Large-Scale Visual Concept Detection and Annotation Task in MULTILINGUAL INFORMATION ACCESS EVALUATION II. MULTIMEDIA EXPERIMENTS
  • 2008. Web-Based Measure of Semantic Relatedness in WEB INFORMATION SYSTEMS ENGINEERING - WISE 2008
  • 2009. Using Second Order Statistics to Enhance Automated Image Annotation in ADVANCES IN INFORMATION RETRIEVAL
  • 2005. Automated Image Annotation Using Global Features and Robust Nonparametric Density Estimation in IMAGE AND VIDEO RETRIEVAL
  • Book

    TITLE

    Multilingual Information Access Evaluation II. Multimedia Experiments

    ISBN

    978-3-642-15750-9
    978-3-642-15751-6

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15751-6_40

    DOI

    http://dx.doi.org/10.1007/978-3-642-15751-6_40

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1000579645


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