期刊
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
卷 -, 期 -, 页码 -出版社
SPRINGER
DOI: 10.1186/s13640-018-0322-6
关键词
Image segmentation; K-mean; Clustering; Gray-level co-occurrence matrix (GLCM); Maximum entropy
资金
- 973 Key National Basic Research Program of China [2015CB251602]
- National Natural Science Foundation of China [51504184, 51604264]
- China Postdoctoral Science Foundation [2017M196372XB]
- Doctoral and Post-doctoral Start Foundation of Xi'an University of Science and Technology [2016QDJ048, 2017QDJ060]
- Open Projects of Research Center of Coal Resources Safe Mining and Clean Utilization, Liaoning [LNTU17KF08]
- Natural Science Foundation of Shaanxi Province [2018JQ5194]
The image is an important way for people to understand the world. How to make the computer have image recognition function is the goal of image recognition research. In image recognition, image segmentation technology is one of the important research directions. This paper uses gray-gradient maximum entropy method to extract features from the image, uses K-mean method to classify the images, and uses average precision (AP) and intersection over union (IU) evaluation methods to evaluate the results. The results show that the method of K-mean can achieve image segmentation very well.
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