Paper Info
Title
Nonparametric bottom-up saliency detection using hypercomplex spectral contrast
Abstract
Saliency detection is an useful technique for image semantic analysis such as auto image segmentation, image retargeting, advertising design and image compression. Inspired by two existing saliency detection algorithms, named spectral residual (SR) and phase spectrum of quaternion Fourier transform (PQFT), we propose a new bottom-up saliency detection method which is featured with the introduction of hypercomplex spectral contrast (HSC) in saliency detection. The proposed HSC algorithm introduces the HSV color image vector space in hypercomplex number, and is better comprehensive to consider amplitude spectral contrast into saliency model as well as phase spectral contrast. Meanwhile, we also incorporate the human vision nonuniform sampling into our model, which is a common phenomenon that directs visual attention to the logarithmic center of image in natural scenes. Experimental results on two public saliency detection datasets show that our approach performs better than four state-of-the art approaches remarkably.
Year
DOI
Venue
2011
10.1145/2072298.2071963
ACM Multimedia 2001
Keywords
Field
DocType
saliency detection,hsv color image vector,existing saliency detection algorithm,image retargeting,image compression,saliency model,new bottom-up saliency detection,hypercomplex spectral contrast,auto image segmentation,public saliency detection datasets,image semantic analysis,spectrum,vector space,image segmentation,bottom up,nonuniform sampling,fourier transform,color image
HSL and HSV,Computer vision,Kadir–Brady saliency detector,Pattern recognition,Computer science,Salience (neuroscience),Seam carving,Hypercomplex number,Image segmentation,Artificial intelligence,Image compression,Color image
Conference
Citations 
PageRank 
References 
3
0.37
10
Authors
4
Name
Order
Citations
PageRank
Ce Li1569.28
J. Xue254257.57
Nanning Zheng33975329.18
Zhiqiang Tian48120.68