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Regional Principal Color Based Saliency Detection

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  • Jing Lou
  • Mingwu Ren
  • Huan Wang

Abstract

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.

Suggested Citation

  • Jing Lou & Mingwu Ren & Huan Wang, 2014. "Regional Principal Color Based Saliency Detection," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-13, November.
    • Handle: RePEc:plo:pone00:0112475
    DOI: 10.1371/journal.pone.0112475
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    Cited by:

    1. Amirhossein Aghamohammadi & Mei Choo Ang & Elankovan A. Sundararajan & Ng Kok Weng & Marzieh Mogharrebi & Seyed Yashar Banihashem, 2018. "A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.
    2. Wei Zhu & Jing Lou & Longtao Chen & Qingyuan Xia & Mingwu Ren, 2017. "Scene text detection via extremal region based double threshold convolutional network classification," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-17, August.

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