Paper Info
Title
Visual Saliency Detection Via Background Features and Object-Location Cues
Abstract
In this work, we describe a simple visual saliency-detection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are selected to represent directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to create a credible saliency map. Simulation experiments verify that the designed algorithm is useful and effective.
Year
DOI
Venue
2019
10.23919/IConAC.2019.8895226
2019 25th International Conference on Automation and Computing (ICAC)
Keywords
Field
DocType
discrete wavelet transform,saliency detection,background features,position prior
Saliency map,Dictionary learning,Pattern recognition,Salient objects,Control engineering,Discrete wavelet transform,Artificial intelligence,Engineering,Wavelet,Visual saliency
Conference
ISBN
Citations 
PageRank 
978-1-7281-2518-3
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Muwei Jian123530.97
Jing Wang210.71
Hui Yu312821.50