Saliency detection using texture and local cues

Qiang Qi, Muwei Jian, Yilong Yin, Junyu Dong, Wenyin Zhang, Hui Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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In this paper, a simple but effective method is proposed for detecting salient objects by utilizing texture and local cues. In contrast to the existing saliency detection models, which mainly consider visual features such as orientation, color, and shape information, our proposed method takes the significant texture cue into consideration to guarantee the accuracy of the detected salient regions. Firstly, an effective method based on selective contrast (SC), which explores the most distinguishable component information in texture, is used to calculate the texture saliency map. Then, we detect local saliency by using a locality-constrained linear coding algorithm. Finally, the output saliency map is computed by integrating texture and local saliency cues simultaneously. Experimental results, based on a widely used and openly available database, demonstrate that the proposed method can produce competitive results and outperforms some existing popular methods.
Original languageEnglish
Title of host publicationComputer Vision
Subtitle of host publicationSecond CCF Chinese Conference, CCCV 2017, Tianjin, China, October 11–14, 2017, Proceedings, Part III
EditorsJinfeng Yang, Qinghua Hu, Ming-Ming Cheng, Liang Wang, Qingshan Liu, Xiang Bai, Deyu Meng
ISBN (Electronic)978-981-10-7305-2
ISBN (Print)978-981-10-7304-5
Publication statusPublished - Dec 2017
EventCCF Chinese Conference on Computer Vision - Tianjin, China
Duration: 11 Oct 201714 Oct 2017

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


ConferenceCCF Chinese Conference on Computer Vision
Abbreviated titleCCCV 2017


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