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Extended Locality-Constrained Linear Self-Coding for Saliency Detection

delete2017-10-01
delete3
PRE
AI
C
Chunlei Yang
J
Jiexin Pu *
G
Guo-Sen Xie
Y
Yongsheng Dong
刘忠华 cover
刘忠华 (Zhonghua Liu)
DOI：10.1109/LSP.2017.2737650delete
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Abstract

Abstract

En 中文
In complex scenes, foreground saliency can hardly be detected completely, which may further result in the ambiguous cues of objects for other computer vision tasks. In this letter, an extended locality-constrained linear self-coding (eLLsC) scheme is proposed to assist to solve the saliency detection problem under the complex scenes. The locality of both spatial relation and feature distance is preserved in eLLsC, thus making the transformed code involved in the manifold ranking to prompt the generation of the saliency map with more complete foreground and clearer boundary. Experimental results on three saliency detection benchmarks demonstrate the effectiveness of the proposed hybrid method.
Keywords:
Complex scene
linear self-coding
locality-constrained
manifold ranking
saliency detection
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Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

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