Title
Visual saliency detection by integrating spatial position prior of object with background cues
Abstract
In this paper, we propose an effective visual saliency-detection model based on spatial position prior of attractive objects and sparse background features. Firstly, since multi-orientation features are among the key visual stimuli in the human visual system (HVS) to perceive object spatial information, discrete wavelet frame transform (DWDT) is applied to extract directionality characteristics for calculating the centoid of remarkable objects in the original image. Then, the color contrast feature is used to represent the physical characteristics of salient objects. Thirdly, in order to explore and utilize the background features of an input image, sparse dictionary learning is performed to statistically analyze and estimate the background feature map. Finally, three distinctive cues of the directional feature including the color contrast feature and the background feature are combined to generate a final robust saliency map. Experimental results on three widely used image datasets show that our proposed method is effective and efficient, and is superior to other state-of-the-art saliency-detection models.
Year
DOI
Venue
2021
10.1016/j.eswa.2020.114219
Expert Systems with Applications
Keywords
DocType
Volume
Discrete wavelet transform,Saliency detection,Background features,Position prior
Journal
168
ISSN
Citations 
PageRank 
0957-4174
1
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Muwei Jian123530.97
Jing Wang262.11
Hui Yu3399.98
Guodong Wang4284.06
Xiangjing Meng510.34
Lu Yang642.43
Junyu Dong79923.43
Yilong Yin8966135.80