Using Visual Attention in a CBIR System - Experimental Results on Landmark and Object Recognition Tasks

Franco Alberto Cardillo, Giuseppe Amato, Fabrizio Falchi

Abstract

Many novel applications in the field of object recognition and pose estimation have been built relying on local invariant features extracted from key points that rely on high-contrast regions of the images. The visual saliency of the those regions is not considered by state-of-the art detection algorithms that assume the user is interested in the whole image. In this paper we present the experimental results of the application of a biologically-inspired model of visual attention to the problem of local feature selection in landmark and object recognition tasks. The results show that the approach improves the accuracy of the classifier in the object recognition task and preserves a good accuracy in the landmark recognition task.

References

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Paper Citation


in Harvard Style

Cardillo F., Amato G. and Falchi F. (2013). Using Visual Attention in a CBIR System - Experimental Results on Landmark and Object Recognition Tasks . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 468-471. DOI: 10.5220/0004299404680471


in Bibtex Style

@conference{visapp13,
author={Franco Alberto Cardillo and Giuseppe Amato and Fabrizio Falchi},
title={Using Visual Attention in a CBIR System - Experimental Results on Landmark and Object Recognition Tasks},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={468-471},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004299404680471},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Using Visual Attention in a CBIR System - Experimental Results on Landmark and Object Recognition Tasks
SN - 978-989-8565-47-1
AU - Cardillo F.
AU - Amato G.
AU - Falchi F.
PY - 2013
SP - 468
EP - 471
DO - 10.5220/0004299404680471