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Prosemantic Image Retrieval

  • Gianluigi Ciocca
  • Claudio Cusano
  • Simone Santini
  • Raimondo Schettini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

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.

Keywords

Image Retrieval Relevance Feedback Relevant Image Text Annotation Ancillary Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ciocca, G., Cusano, C., Santini, S., Schettini, R.: Halfway through the semantic gap: prosemantic features for image retrieval. Inf. Sciences 181, 4943–4958 (2011)CrossRefGoogle Scholar
  2. 2.
    Santini, S., Cusano, C., Schettini, R.: Diversity and novelty in multimedia search. In: ACM Multimedia 2012 (submitted, 2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gianluigi Ciocca
    • 1
  • Claudio Cusano
    • 1
  • Simone Santini
    • 2
  • Raimondo Schettini
    • 1
  1. 1.Università degli Studi di Milano-BicoccaMilanoItaly
  2. 2.Universidad Autónoma de MadridMadridSpain

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