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Environmental problems related to microorganisms are receiving increasing attention. Compared with manual judgement and instrumental detection, processing microbial microscopic images for microbial retrieval using computer image processing techniques is a more effective method. In this paper, GLCM, GGCM, HOG, LBP and Gabor features of microbial microscopic images are extracted respectively. A microbial microscopic image retrieval method based on texture features is proposed using machine learning methods, and the retrieval effect of the fusion of two texture features is particularly analysed. By comparing the retrieval effect of multiple texture features, the HOG-based retrieval method has superior performance.

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Texture Features Based Microbiological Image Retrieval

  • 会议论文

  • 发表日期：

    2025年

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  • Han Yu,
  • Bolin Lu,
  • Xinyu Ouyang,
  • Yuhang Yang,
  • Yue Zhang,
  • Haobo Meng,
  • Marcin Grzegorzek,
  • Xin Zhao,
  • Chen Li,
  • Hongwei Lei

摘要

Environmental problems related to microorganisms are receiving increasing attention. Compared with manual judgement and instrumental detection, processing microbial microscopic images for microbial retrieval using computer image processing techniques is a more effective method. In this paper, GLCM, GGCM, HOG, LBP and Gabor features of microbial microscopic images are extracted respectively. A microbial microscopic image retrieval method based on texture features is proposed using machine learning methods, and the retrieval effect of the fusion of two texture features is particularly analysed. By comparing the retrieval effect of multiple texture features, the HOG-based retrieval method has superior performance.