Paper Info
Title
Spatiotemporal Saliency Detection Using Textural Contrast and Its Applications
Abstract
Saliency detection has been extensively studied due to its promising contributions for various computer vision applications. However, most existing methods are easily biased toward edges or corners, which are statistically significant, but not necessarily relevant. Moreover, they often fail to find salient regions in complex scenes due to ambiguities between salient regions and highly textured backgrounds. In this paper, we present a novel unified framework for spatiotemporal saliency detection based on textural contrast. Our method is simple and robust, yet biologically plausible; thus, it can be easily extended to various applications, such as image retargeting, object segmentation, and video surveillance. Based on various datasets, we conduct comparative evaluations of 12 representative saliency detection models presented in the literature, and the results show that the proposed scheme outperforms other previously developed methods in detecting salient regions of the static and dynamic scenes.
Year
DOI
Venue
2014
10.1109/TCSVT.2013.2290579
IEEE Trans. Circuits Syst. Video Techn.
Keywords
DocType
Volume
textural contrast,computer vision applications,image segmentation,image retargeting,saliency detection,salient regions,object segmentation,human visual attention,comparative evaluations,edge detection,computer vision,spatiotemporal saliency detection,image texture,video surveillance,coherence,computational modeling,visualization,robustness
Journal
24
Issue
ISSN
Citations 
4
1051-8215
21
PageRank 
References 
Authors
0.73
35
2
Name
Order
Citations
PageRank
Wonjun Kim130126.50
Changick Kim296364.78