Abstract
Image is an important means for human to cognize the world, and image processing technology is also a key research direction in machine learning. In image processing technology, image segmentation is a very critical part of the current academic research hotspot. At present, the fuzzy C-means clustering (FCM) algorithm of image segmentation algorithm uses iterative method to classify samples, which needs less storage space and time. However, FCM algorithm also has many shortcomings, how to use clustering algorithm for real-time, automatic, high-quality image segmentation, has been a problem to be solved. In order to solve the massive data of color image, this paper uses the SLIC method to calculate the super-pixel image over-segmentation. Direct processing of the huge amount of information contained in a color image will degrade the performance of the algorithm. Therefore, image preprocessing is very important.
The 2020 Heilongjiang University basic scientific research project “Feature learning model for forest fire image recognition and classification in northeast China” School-level topics(2020-KYYWF-0885)
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, Y., Yang, D., Zhang, B., Zhai, Z., Luo, Z. (2023). Image Segmentation Based on Fuzzy Method. In: Jiang, X. (eds) Machine Learning and Intelligent Communication. MLICOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 481. Springer, Cham. https://doi.org/10.1007/978-3-031-30237-4_15
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DOI: https://doi.org/10.1007/978-3-031-30237-4_15
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