Paper Info
Title
Region contrast and supervised locality-preserving projection-based saliency detection.
Abstract
As an important problem in computer vision, saliency detection is essential for image segmentation, super-resolution, object recognition, etc. In this paper, we propose a novel method for saliency detection on image using region contrast and machine learning approaches. An image boundary extension-based general framework is proposed that can be used for all rarity- or sparsity-based schemes to improve their performances. Then, a saliency map based on boundary extension and region contrast is constructed. Due to its unsatisfactory performance, another saliency map combining supervised locality-preserving projection and support vector regression is built, to complement the previous saliency map. A final saliency map can be obtained by fusing these two saliency maps. The proposed method is evaluated on the publicly available dataset MSRA-1000 and compared with 13 state-of-the-art methods. Experimental results indicate that the proposed method outperforms existing schemes both in qualitative and quantitative comparisons.
Year
DOI
Venue
2015
10.1007/s00371-014-1005-7
The Visual Computer: International Journal of Computer Graphics
Keywords
Field
DocType
Saliency detection, Region contrast, Boundary extension, Supervised locality-preserving projection, Support vector regression
Computer vision,Locality,Saliency map,Kadir–Brady saliency detector,Pattern recognition,Salience (neuroscience),Computer science,Support vector machine,Image segmentation,Artificial intelligence,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
31
9
1432-2315
Citations 
PageRank 
References 
5
0.39
36
Authors
6
Name
Order
Citations
PageRank
Yanjiao Shi1343.14
Yugen Yi29215.25
Hexin Yan350.39
Jiangyan Dai4144.19
Ming Zhang5161.93
Jun Kong6274.86