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