Spatiotemporal Saliency Detection Using Textural Contrast and Its Applications

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2014-04
Language
English
Article Type
Article
Keywords

VISUAL-ATTENTION; IMAGE SEGMENTATION; OBJECT DETECTION; REGION DETECTION; DYNAMIC SCENES; MODEL; VIDEO; SUBTRACTION; TRACKING

Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.24, no.4, pp.646 - 659

ISSN
1051-8215
DOI
10.1109/TCSVT.2013.2290579
URI
http://hdl.handle.net/10203/189205
Appears in Collection
EE-Journal Papers(저널논문)
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