scholarly journals A Novel Method for Multilevel Color Image Segmentation Based on Dragonfly Algorithm and Differential Evolution

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 19502-19538 ◽  
Author(s):  
Lang Xu ◽  
Heming Jia ◽  
Chunbo Lang ◽  
Xiaoxu Peng ◽  
Kangjian Sun
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Color Image ◽  
Color Image Segmentation ◽  
Dragonfly Algorithm ◽  
Novel Method

Research on Multi-Threshold Color Image Segmentation Based on Rough Set

2011 ◽  
Vol 474-476 ◽  
pp. 771-776
Author(s):  
Guo Quan Zhang ◽  
Zhan Ming Li
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Color Image ◽  
Experimental Results ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Illumination Variation ◽  
Threshold Segmentation ◽  
Segmentation Accuracy ◽  
Novel Method

Aims at the problem that the threshold number and value are difficulty to determine automatically existing in multi-threshold color image segmentation method, a novel method of multi-threshold segmentation in HSV is proposed. First of all, the image is pre-processed in HSV, component H and V is projected to S and be quantified at the same time. Secondly, histogram and advanced Histon histogram (AHH) are constructed. According to concept of roughness in the theory of Rough Set, the histogram of roughness (RSH) is constructed. Finally, according to requirement of segmentation accuracy, set a threshold Hn on RSH to determine the number and scope of multi-threshold and the image is segmented with above thresholds. The experimental results show that this method can determine the threshold quantity automatically, segment image efficiently and robust against illumination variation.

scholarly journals Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 716 ◽  
Author(s):  
Xiaoli Bao ◽  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Search Space ◽  
Color Image Segmentation ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Dragonfly Algorithm ◽  
Opposition Based Learning

Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

scholarly journals Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

2016 ◽  
Vol 6 (5) ◽  
pp. 1182-1186
Author(s):  
R. V. V. Krishna ◽  
S. Srinivas Kumar
Keyword(s):  
Genetic Algorithms ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Color Image ◽  
Texture Feature ◽  
Color Image Segmentation ◽  
Cluster Center ◽  
Local Descriptor ◽  
Color Feature ◽  
Real Value

This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD) which is a modification of Weber Local Descriptor (WLD) is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.

Roughness Measure Approach to Color Image Segmentation

2010 ◽  
Vol 36 (6) ◽  
pp. 807-816 ◽  
Author(s):  
Xiao-Dong YUE ◽  
Duo-Qian MIAO ◽  
Cai-Ming ZHONG
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation

Color image segmentation based on mean shift and multi-feature fusion

2009 ◽  
Vol 29 (8) ◽  
pp. 2074-2076
Author(s):  
Hua LI ◽  
Ming-xin ZHANG ◽  
Jing-long ZHENG
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Color Image ◽  
Mean Shift ◽  
Color Image Segmentation
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