Research Article
Clustering Analysis Based on Segmented Images
@INPROCEEDINGS{10.1007/978-3-319-73564-1_7, author={Hongxu Zheng and Jianlun Wang and Can He}, title={Clustering Analysis Based on Segmented Images}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Image segmentation Resolution adjustment Gray-level co-occurrence matrix Clustering analysis}, doi={10.1007/978-3-319-73564-1_7} }- Hongxu Zheng
Jianlun Wang
Can He
Year: 2018
Clustering Analysis Based on Segmented Images
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_7
Abstract
Image segmentation plays an important role in the field of digital production management. Image resolution is an important factor affecting the size of its segmentation and segmentation efficiency, and the physical characteristics of the image capturing device is another important factor. With high-resolution segmentation algorithm in image segmentation, we often find that the edge contour image segmentation is difficult to accurately determine, more complex image arithmetic operation efficiency is not high and images taken with a different device in response to segmentation algorithms are very different. In this paper, the plant leaf image collected from different cameras was used as the object of study, and the feature quantity was extracted. The appropriate segmentation boundary was determined by cluster analysis. The leaf image was pretreated with the resolution adjustment, and the leaf image was in the appropriate segmentation feature range. After the clustering domain processing of the feature range in this paper, it solves the problem that the real edge of the leaf area information is too difficult to distinguish, and effectively solves the problem of complex image algorithm and ordinary pc machine in the process of complex image processing Efficiency issues. The appropriate segmentation feature range of the devices is established for different devices, which effectively solves the different response of different devices to the segmentation algorithm.
