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Fuzzy C-means Clustering Image Segmentation Algorithm Based on Hidden Markov Model

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Abstract

Aiming at the poor anti-jamming effect of traditional fuzzy c-means clustering image segmentation method, a fuzzy c-means clustering image segmentation algorithm based on Hidden Markov model is proposed. Hidden Markov model is used to extract the model state, observation state, initial probability, transition probability and visible symbol probability parameters of fuzzy c-means clustering image. According to these parameters, the image texture is measured, and the image mean is removed to eliminate the influence of brightness. Then the image features are extracted by combining the standard deviation. Through the color space and convert it from RGB model to his model. On the basis of removing the image noise, the image is smoothed to achieve image segmentation. Experimental results show that the algorithm has good segmentation effect, smooth segmentation boundary, little influence of image noise and strong robustness.

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Acknowledgements

The research is supported by Ministry of Education Science and Technology Development Research Center College Industry-University-Research Innovation Fund (2018A02038).

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  1. College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing, 408100, China

    Ru Xu

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  1. Ru Xu
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Correspondence to Ru Xu.

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Xu, R. Fuzzy C-means Clustering Image Segmentation Algorithm Based on Hidden Markov Model. Mobile Netw Appl 27, 946–954 (2022). https://doi.org/10.1007/s11036-022-01917-7

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