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Salient Object Detection via Saliency Spread

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

Salient object detection aims to localize the most attractive objects within an image. For such a goal, accurately determining the saliency values of image regions and keeping the saliency consistency of interested objects are two key challenges. To tackle the issues, we first propose an adaptive combination method of incorporating texture with the dominant color, for enriching the informativeness and discrimination of features, and then propose saliency spread to encourage the image regions of the same object producing equal saliency values. In particular, saliency spread propagates the saliency values of the most salient regions to their similar regions, where the similarity serves for measuring the degree of belonging to the same object of different regions. Experimental results on the benchmark database MSRA-1000 show that our proposed method can produce more consistent saliency maps, which is beneficial to accurately segment salient objects, and is quite competitive compared with the advanced methods in previous literatures.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Dao Xiang & Zilei Wang

Authors
  1. Dao Xiang
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  2. Zilei Wang
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Correspondence to Zilei Wang .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Xiang, D., Wang, Z. (2015). Salient Object Detection via Saliency Spread. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_33

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

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