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Prosemantic Features for Content-Based Image Retrieval

  • Gianluigi Ciocca
  • Claudio Cusano
  • Simone Santini
  • Raimondo Schettini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6535)

Abstract

We present here, an image description approach based on prosemantic features. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. To verify the effectiveness of the approach, we designed a target search experiment in which both low-level and prosemantic features are embedded into a content-based image retrieval system exploiting relevance feedback. The experiments show that the use of prosemantic features allows for a more successful and quick retrieval of the query images.

Keywords

Support Vector Machine Image Retrieval Target Image Relevance Feedback Color Histogram 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gianluigi Ciocca
    • 1
  • Claudio Cusano
    • 1
  • Simone Santini
    • 2
  • Raimondo Schettini
    • 1
  1. 1.Dipartimento di Informatica Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanoItaly
  2. 2.Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

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