Paper Info
Title
Saliency Detection Via Combining Global Shape And Local Cue Estimation
Abstract
Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency-detection algorithms.
Year
DOI
Venue
2017
10.1007/978-3-319-67777-4_28
INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017
Keywords
Field
DocType
Saliency detection, QDWD, Locality-constrained linear coding, Local cue
Computer vision,Saliency map,Pattern recognition,Salience (neuroscience),Computer science,Salient objects,Outlier,Linear coding,Reconstruction error,Artificial intelligence
Conference
Volume
ISSN
Citations 
10559
0302-9743
0
PageRank 
References 
Authors
0.34
18
6
Name
Order
Citations
PageRank
Qiang Qi1362.59
Muwei Jian223530.97
Yilong Yin3966135.80
Junyu Dong439377.68
Wenyin Zhang5206.41
Hui Yu612821.50