计算机与现代化

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基于萤火虫算法的无监督最小视觉差彩色图像分割

  

  1. (陕西师范大学计算机科学学院,陕西西安710119)
  • 收稿日期:2018-05-31 出版日期:2019-01-03 发布日期:2019-01-04
  • 作者简介:孙源(1993-),女,吉林梅河口人,陕西师范大学计算机科学学院硕士研究生,研究方向:模式识别和图像处理; 刘汉强(1981-),男,山东莱芜人,副教授,博士,研究方向:模式识别和图像处理。
  • 基金资助:
    国家自然科学基金资助项目(61202153, 41471280); 中央高校基本科研业务费专项资金资助项目(GK201503063)

An Unsupervised JND Color Image Segmentation Based on Firefly Algorithm

  1. (School of Computer Science, Shaanxi Normal University, Xi’an 710119, China)
  • Received:2018-05-31 Online:2019-01-03 Published:2019-01-04

摘要: 针对传统阈值分割算法中阈值个数的选择问题,提出一种基于萤火虫算法的无监督最小视觉差彩色图像分割方法。首先,从图像中自动获取差异性比较大的像素作为后续步骤的监督信息,随后,设计萤火虫算法对基于像素的邻域信息和最小视觉差理论的阈值进行优化选择,最后,采用峰值信噪比和概率边缘信息对分割性能相当的结果进一步评价进而选择最终的分割结果。对实际图像的分割实验表明,本文算法在分割效果上和鲁棒性都有了极大的提高。

关键词: 萤火虫算法, 最小视觉差, 邻域信息, 图像分割

Abstract: In view of the traditional threshold segmentation algorithm, the choice of threshold number has important effect to the result of color image segmentation. We present an unsupervised JND color image segmentation based on firefly algorithm. First, getting samples from the inputing image, we obtain the neighborhood information of the center pixel and automatically obtaining the supervised pixel information. Then, use the firefly algorithm with the supervised pixel information to select the suitable threshold for color image informentation and use the index to evaluate the segmentation performance, such as the peak signal-to-noise ratio and the probability edge information. The performance of color image is segmented by introducing the theory of just noticeable difference, firefly algorithm. Moreover, the robustness of the algorithm is greatly improved.

Key words:  firefly algorithm, just noticeable difference, neighborhood information, image segmentation

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