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Standard Deviation Clustering Combined with Visual Psychological Test Algorithm for Image Segmentation

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Advanced Data Mining and Applications (ADMA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11888))

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Abstract

Detection of the visual salient image area for image segmentation, image recognition, and adaptive compression application is beneficial. It makes an object, a person, or some pixels stand out against the background of the image and provide support for image recognition and target detection. The detection can simplify the process of computer visual image processing and improve the effect and efficiency of computer visual inspection. This paper introduces a kind of salient detection method, without any manual intervention, and uses the method of decomposing brightness, color space, negative map solution, and standard deviation to find the super-distance pixel in the image. The method of clustering is used to separate the region of objects and image background, and output RGB color salient objects image. Moreover, it can accurately highlight the object contour and internal pixels. This method studies the characteristics of the original pixels such as brightness or color and utilizes the image basis features to achieve the image saliency detection. It has high adaptive detection ability, low time complexity and high computational efficiency.

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Authors and Affiliations

  1. Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610041, China

    Zhenggang Wang, Jin Jin & Zhong Liu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhenggang Wang & Jin Jin

  3. Chengdu Customs District of People’s Republic of China, Chengdu, 610032, China

    Zhenggang Wang

  4. Leshan Vocational and Technical College, Leshan, 614000, China

    Zhong Liu

Authors
  1. Zhenggang Wang
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  2. Jin Jin
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  3. Zhong Liu
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Corresponding author

Correspondence to Jin Jin .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  2. The University of Queensland, St. Lucia, QLD, Australia

    Sen Wang

  3. Flinders University, Bedford Park, SA, Australia

    Shaowen Qin

  4. Dalian Neusoft University of Information, Dalian, China

    Xue Li

  5. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Wang, Z., Jin, J., Liu, Z. (2019). Standard Deviation Clustering Combined with Visual Psychological Test Algorithm for Image Segmentation. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_37

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  • DOI: https://doi.org/10.1007/978-3-030-35231-8_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35230-1

  • Online ISBN: 978-3-030-35231-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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