Open AccessFast visual saliency based on multi‐scale difference of Gaussians fusion in frequency domain
Author(s) -
Li Weipeng,
Yang Xiaogang,
Li Chuanxiang,
Lu Ruitao,
Xie Xueli
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0773
Subject(s) - frequency domain , fourier transform , artificial intelligence , scale (ratio) , feature (linguistics) , filter (signal processing) , scale space , algorithm , pattern recognition (psychology) , computation , computer science , mathematics , fusion , channel (broadcasting) , computer vision , image (mathematics) , image processing , mathematical analysis , physics , computer network , linguistics , philosophy , quantum mechanics
To reduce the computation required in determining the proper scale of salient object, a fast visual saliency based on multi‐scale difference of Gaussians fusion in frequency domain (MDF) is proposed. First, based on the phenomenon that the foreground energy is highlighted and densely distributes on certain band of spectrum, the scale coefficients of foreground in an image can be literately approximated on the amplitude spectrum. Next, relying on the linear integration property of Fourier transform, the feature spectrum is obtained through the weighted infinite integral of difference of Gaussian feature maps with respect to the scale of object. Then, the saliency of each channel is obtained from feature spectrum by the inverse Fourier transform and scale filtering. Finally, through the channel integration, the MDF saliency map is obtained. Experiments on Li‐Jian data set demonstrate that combined with most appropriate colour space and scale filter, MDF achieves obvious acceleration (5.4 times faster than frequency domain analysis and spatial information) while getting desired accuracy (area under the curve, 0.8814 at Li‐Jian data set), which achieves the best accuracy efficiency trade‐off.