Stentiford, F;
(2013)
Saliency identified by absence of background structure.
In:
Human Vision and Electronic Imaging XVIII.
(pp. 86510V).
SPIE: Burlingame, California, U.S..
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Abstract
Visual attention is commonly modelled by attempting to characterise objects using features that make them special or in some way distinctive in a scene. These approaches have the disadvantage that it is never certain what features will be relevant in an object that has not been seen before. This paper provides a brief outline of the approaches to modeling human visual attention together with some of the problems that they face. A graphical representation for image similarity is described that relies on the size of maximally associative structures (cliques) that are found to be reflected in pairs of images. While comparing an image with itself, the similarity mechanism is shown to model pop-out effects when constraints are placed on the physical separation of pixels that correspond to nodes in the maximal cliques. Background regions are found to contain structure in common that is not present in the salient regions which are thereby identified by its absence. The approach is illustrated with figures that exemplify asymmetry in pop-out, the conjunction of features, orientation disturbances and the application to natural images.
Type: | Proceedings paper |
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Title: | Saliency identified by absence of background structure |
Event: | IS&T/SPIE Electronic Imaging 2013 |
Location: | San Francisco, US |
Dates: | 03 February 2013 - 07 February 2013 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1117/12.2002085 |
Publisher version: | http://dx.doi.org/10.1117/12.2002085 |
Language: | English |
Additional information: | © 2013. Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Visual attention, saliency, pattern recognition, similarity, pop-out, cliques |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1546243 |
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