Attentive Content-Based Image Retrieval View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Dounia Awad , Vincent Courboulay , Arnaud Revel

ABSTRACT

In this chapter, we show how visual attention can enhance the performance in object recognition. This proposition is inspired by the idea introduced in Neisser (Cognitive psychology. Appleton-Century-Crofts, New York, 1967), which indicates that object recognition in human perception consists of two steps: an “attentional process selects the region of interest” and “complex object recognition processes are restricted to these regions”. Recently, in computer vision, much work has been done to combine the two domains of visual attention and object recognition, which we will call “attentive content-based image retrieval”. The test on our attentive CBIR approach in VOC 2005 demonstrated that we can maintain approximately the same recognition performance by selecting only 40 % of SIFT keypoints using classical saliency models. The proposed attentive CBIR framework can also be used to make a ranking between existing saliency models when used for CBIR. This ranking is different from the one using classical ground-truth like eye-tracking which means that choosing the best saliency models in predicting eye-tracking might be misleading when focusing on a CBIR application. More... »

PAGES

379-398

References to SciGraph publications

  • 2012. Saliency Filtering of SIFT Detectors: Application to CBIR in ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS
  • 2010-06. The Pascal Visual Object Classes (VOC) Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004-10. Scale & Affine Invariant Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1987. Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry in MATTERS OF INTELLIGENCE
  • 2011-06. Towards attentive robots in PALADYN
  • 2010. VF-SIFT: Very Fast SIFT Feature Matching in PATTERN RECOGNITION
  • 1994-06. Guided Search 2.0 A revised model of visual search in BULLETIN OF THE PSYCHONOMIC SOCIETY
  • 2007. Generating Sequence of Eye Fixations Using Decision-Theoretic Attention Model in ATTENTION IN COGNITIVE SYSTEMS. THEORIES AND SYSTEMS FROM AN INTERDISCIPLINARY VIEWPOINT
  • 2002-11-21. Visual Attention Using Game Theory in BIOLOGICALLY MOTIVATED COMPUTER VISION
  • Book

    TITLE

    From Human Attention to Computational Attention

    ISBN

    978-1-4939-3433-1
    978-1-4939-3435-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-1-4939-3435-5_19

    DOI

    http://dx.doi.org/10.1007/978-1-4939-3435-5_19

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1031739845


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