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Saliency Detection Model for Low Contrast Images Based on Amplitude Spectrum Analysis and Superpixel Segmentation

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Book cover Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

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Abstract

Traditional saliency detection models face great challenges towards low contrast images with low signal-to-noise ratio property. In this circumstance, it is difficult to extract effective visual features to describe salient information in image. This paper proposes a saliency detection model for low contrast images utilizing efficient features both from frequency domain and spatial domain. The input image is firstly transformed into frequency domain to calculate the amplitude spectrum by a median filter, aiming to suppress the information from non-salient regions. Then, a superpixel based feature extraction method is utilized to generate saliency map via both local and global spatial information. Experiments are carried on the low contrast image dataset to demonstrate the effectiveness of the proposed saliency detection model over other eight state-of-the-art saliency models.

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Acknowledgments

This work was supported by the Natural Science Foundation of China (61602349, 61373109, 61403287, 61602350 and 61273225) and the China Scholarship Council (201508420248).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Hua Yang, Xin Xu & Nan Mu

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, China

    Xin Xu

Authors
  1. Hua Yang
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  2. Xin Xu
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  3. Nan Mu
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Corresponding author

Correspondence to Xin Xu .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Yang, H., Xu, X., Mu, N. (2016). Saliency Detection Model for Low Contrast Images Based on Amplitude Spectrum Analysis and Superpixel Segmentation. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_56

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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