Paper Info
Title
Finding the Secret of Image Saliency in the Frequency Domain
Abstract
There are two sides to every story of visual saliency modeling in the frequency domain. On the one hand, image saliency can be effectively estimated by applying simple operations to the frequency spectrum. On the other hand, it is still unclear which part of the frequency spectrum contributes the most to popping-out targets and suppressing distractors. Toward this end, this paper tentatively explores the secret of image saliency in the frequency domain. From the results obtained in several qualitative and quantitative experiments, we find that the secret of visual saliency may mainly hide in the phases of intermediate frequencies. To explain this finding, we reinterpret the concept of discrete Fourier transform from the perspective of templatebased contrast computation and thus develop several principles for designing the saliency detector in the frequency domain. Following these principles, we propose a novel approach to design the saliency detector under the assistance of prior knowledge obtained through both unsupervised and supervised learning processes. Experimental results on a public image benchmark show that the learned saliency detector outperforms 18 state-of-the-art approaches in predicting human fixations.
Year
DOI
Venue
2015
10.1109/TPAMI.2015.2424870
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keywords
Field
DocType
Image saliency, Fourier transform, spectral analysis, fixation prediction, learning-based, experimental study
Frequency domain,Computer vision,Kadir–Brady saliency detector,Pattern recognition,Salience (neuroscience),Computer science,Fourier transform,Supervised learning,Artificial intelligence,Discrete Fourier transform,Detector,Computation
Journal
Volume
Issue
ISSN
PP
99
0162-8828
Citations 
PageRank 
References 
23
0.63
45
Authors
5
Name
Order
Citations
PageRank
Jia Li152442.09
Ling-yu Duan21770124.87
Xiaowu Chen360545.05
Tiejun Huang41281120.48
Yonghong Tian51057102.81