DocumentCode :
3405132
Title :
Saliency detection via statistical non-redundancy
Author :
Jain, Abhishek ; Wong, Alexander ; Fieguth, Paul
Author_Institution :
Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON, Canada
fYear :
2012
fDate :
Sept. 30 2012-Oct. 3 2012
Firstpage :
1073
Lastpage :
1076
Abstract :
A novel algorithm based on statistical non-redundancy is proposed for saliency detection in natural images. By modeling site neighbourhoods as realizations of other site neighbourhoods under a Gaussian process, the saliency of any arbitrary site can be characterized by the statistical non-redundancy of its site neighbourhood with respect to other site neighbourhoods in a given image. Preliminary results using natural images show that the proposed method provides improved precision vs. recall characteristics over previous methods such as spectral residuals and spectral whitening.
Keywords :
Gaussian processes; object detection; Gaussian process; arbitrary site; natural images; precision improvement; recall characteristics; saliency detection; site neighbourhood modeling; spectral residuals; spectral whitening; statistical nonredundancy; Computational modeling; Computer vision; Conferences; Object recognition; Pattern recognition; Redundancy; Visualization; image modelling; object detection; saliency; statistical non-redundancy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1522-4880
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2012.6467049
Filename :
6467049
Link To Document :
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