A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm

Wang Xiaosong; Yang Gang

doi:10.13332/j.1000-1522.20180160

Journal of Beijing Forestry University > 2018 > 40(12): 124-131. > DOI: 10.13332/j.1000-1522.20180160

Wang Xiaosong, Yang Gang. A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm[J]. Journal of Beijing Forestry University, 2018, 40(12): 124-131. DOI: 10.13332/j.1000-1522.20180160

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A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm

Wang Xiaosong^1,,
Yang Gang^2, ,

1.
Department of Engineering, Shandong Institute of Business and Technology, Yantai 264005, Shandong, China
2.
School of Information, Beijing Forestry University, Beijing 100083, China

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Received Date: May 14, 2018
Revised Date: September 16, 2018
Published Date: November 30, 2018

Graphical Abstract

Abstract

Abstract

ObjectiveIn order to improve the accuracy of tree image segmentation under natural background, this paper studies how to combine the color and texture features of tree image, and combine clustering and classification algorithm to optimize multi-objective segmentation of tree image.
MethodBased on the tree image feature analysis, this paper proposes a multi-objective tree image segmentation method based on clustering and classification algorithm. Firstly, using the MSCC framework theory, the clustering and classification objective function depends on clustering center simultaneously. Then, the cluster performance evaluation index function and the classification performance evaluation index function were selected. Finally, the multi-objective evolutionary optimization method, NSGA-II algorithm was used to optimize, and the Pareto front-end optimal solution set was obtained. The I index was used to select the optimal solution from the optimal solution set. In this paper, we selected four images taken under the natural background, such as Oriental plane, Platycladus orientalis, pine and apricot, as samples. K-means, Fuzzy C-means, single-objective optimization of clustering objective function, multi-objective optimization using MOPSO method and multi-objective optimization using NSGA-II method were used to segment the sample images.
ResultWhen the number of cluster centers, the size of population and the number of genetic iterations were the same, the value of index I can verify that the proposed segmentation method had significant advantages. Comparing the index I values of four different sample image segmentation, we can see that the result of genetic optimization using HF index as single objective function was better than that using K-means and FCM algorithm alone. The result of MOPSO multi-objective optimization method was better than that of single objective optimization method, but the result of multi-objective function segmentation based on NSGA-II optimization was better than that of MOPSO objective optimization results.
ConclusionThe experimental results show that the segmentation accuracy of the method proposed in this paper is obviously better than that of single-objective optimization segmentation and K-means, Fuzzy c-means and other segmentation methods, the color and texture features of the tree image are better preserved. So the accuracy of segmentation is significantly improved.
- multiple-objective optimization,
- tree image,
- NSGA-II

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References (24)

References

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A multi-objective optimization segmentation method for tree image based on fusion clustering and classification algorithm