Information: Theoretical Model for Saliency Prediction—Application to Attentive CBIR View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Vincent Courboulay , Arnaud Revel

ABSTRACT

This work presents an original informational approach to extract visual information, model attention and evaluate the efficiency of the results. Even if the extraction of salient and useful information, i.e. observation, is an elementary task for human and animals, its simulation is still an open problem in computer vision. In this article, we define a process to derive optimal laws to extract visual information without any constraints or a priori. Starting from saliency definition and measure through the prism of information theory, we present a framework in which we develop an ecological inspired approach to model visual information extraction. We demonstrate that our approach provides a fast and highly configurable model, moreover it is as plausible as existing models designed for high biological fidelity. It proposes an adjustable trade-off between nondeterministic attentional behavior and properties of stability, reproducibility and reactiveness. We apply this approach to enhance the performance in an object recognition task. As a conclusion, this article proposes a theoretical framework to derive an optimal model validated by many experimentations. More... »

PAGES

145-170

References to SciGraph publications

  • 2003-06-18. A Model of Dynamic Visual Attention for Object Tracking in Natural Image Sequences in COMPUTATIONAL METHODS IN NEURAL MODELING
  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2001-11. Saliency, Scale and Image Description in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010-06. The Pascal Visual Object Classes (VOC) Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004-10. Scale & Affine Invariant Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2011-06. Towards attentive robots in PALADYN
  • 2005. Does a Plane Imitate a Bird? Does Computer Vision Have to Follow Biological Paradigms? in BRAIN, VISION, AND ARTIFICIAL INTELLIGENCE
  • 2013-02. Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1987. Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry in MATTERS OF INTELLIGENCE
  • Book

    TITLE

    Visual Content Indexing and Retrieval with Psycho-Visual Models

    ISBN

    978-3-319-57686-2
    978-3-319-57687-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-57687-9_7

    DOI

    http://dx.doi.org/10.1007/978-3-319-57687-9_7

    DIMENSIONS

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