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  • Received: Jun. 17, 2020

    Accepted: Jul. 22, 2020

    Posted: Jan. 1, 2021

    Published Online: Jan. 11, 2021

    The Author Email: Zhao Na (1985383320@qq.com)

    DOI: 10.3788/LOP202158.0210024

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    Cuijun Zhang, Na Zhao. Improved GrabCut Algorithm Based on Probabilistic Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210024

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Laser & Optoelectronics Progress, Vol. 58, Issue 2, 0210024 (2021)

Improved GrabCut Algorithm Based on Probabilistic Neural Network

Zhang Cuijun1,2 and Zhao Na1,*

Author Affiliations

  • 1School of Information Engineering, Hebei GEO University, Shijiazhuang, Hebei 0 50031, China
  • 2Hebei Center for Ecological and Environmental Geology Research, Hebei GEO University, Shijiazhuang, Hebei 0 50031, China

Abstract

Aiming at the low efficiency of GrabCut algorithm in image segmentation, and the problems of under-segmentation and over-segmentation, an improved GrabCut algorithm based on probabilistic neural network (PNN) (PNN_GrabCut) is proposed in this paper. The algorithm replaces the Gaussian mixture model (GMM) in the GrabCut algorithm with PNN model to calculate the weight of t-links to improve the calculation efficiency of the algorithm. By constructing the foreground and background histograms, the pixels with higher pixel values are selected as training samples of the PNN model to improve the segmentation accuracy of the algorithm. In the public ADE20K data set, images are selected for segmentation experiments. The results show that the segmentation accuracy of PNN_GrabCut algorithm is better than other comparison algorithms, and the efficiency is higher. For image segmentation experiments with high similarity between foreground and background, the results show that the segmentation accuracy of PNN_GrabCut algorithm is significantly higher than that of GrabCut algorithm.

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