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Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms

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Abstract

Due to the complexity of underlying data in a color image, retrieval of specific object features and relevant information becomes a complex task. Colour images have different color components and a variety of colour intensity which makes segmentation very challenging. In this paper we suggest a fitness function based on pixel-by-pixel values and optimize these values through evolutionary algorithms like differential evolution (DE), particle swarm optimization (PSO) and genetic algorithms (GA). The corresponding variants are termed GA-SA, PSO-SA and DE-SA; where SA stands for Segmentation Algorithm. Experimental results show that DE performed better in comparison of PSO and GA on the basis of computational time and quality of segmented image.

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References

  1. De S et al (2012) Color image segmentation using parallel OptiMUSIG activation function. Appl. Soft Comput J 12(10):3228–3236

    Article  Google Scholar 

  2. Yue XD, Miao DQ, Zhang N, Cao LB, Wu Q (2012) Multiscale roughness measure for color image segmentation. Inf Sci 216:93–122

    Article  Google Scholar 

  3. Akay B (2012) “A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding”, Appl. Comput. J, Soft

    Google Scholar 

  4. Mohabey A, Ray AK (2000) Rough set theory based segmentation of color images. In: Proceedings of 19th International Conference of North American Fuzzy Information Processing society, 2000, pp 338–342

  5. Park SH, Yun ID, Lee SU (1998) Color image segmentation based on 3-D clustering. Pattern Recogn 31:1061–1076

    Article  Google Scholar 

  6. Derin H, Elliott H (1987) Modelling and segmentation of noisy and textured images using Gibbs random fields, IEEE Trans. On PAMI, vol 9

  7. Dubes RC, Jain AK, Nadabar SG, Chen CC (1990) MRF model-based algorithms for image segmentation. In: Proceedings of 10th ICPR, vol 1. pp 808–814

  8. Bhanu B, Lee S, Das S (1995) Adaptive image segmentation using genetic and hybrid search methods. IEEE Trans Aerospace Electronic Sys 31(4):1268–1290

    Article  Google Scholar 

  9. Bhandarkar SM, Zhang H (1999) Image segmentation using evolutionary computation. IEEE Trans. Evol Comput 3(1):1–21

    Article  Google Scholar 

  10. Bhanu B, Lee S, Ming J (1995) Adaptive image segmentation using a genetic algorithm. IEEE Trans Systems Man Cybern 25(12):1543–1567

    Article  Google Scholar 

  11. Andrey P (1999) Selectionist relaxation: genetic algorithms applied to image segmentation. Imag Vis Comput 17:175–187

    Article  Google Scholar 

  12. Swets DL, Punch B, Weng J (1995) Genetic algorithms for object recognition in a complexscene. In: Proceedings 1995 International Conference Image Processing (ICIP’95) (1995)

  13. Ramos V, Muge F (2000) Image colour segmentation by genetic algorithms. In: Proceedings 11th Portuguese Conference Pattern Recognition, (2000)

  14. Bhandarkar SM, Zhang H (1999) Image segmentation using evolutionary computation. IEEE Trans Evol Comput 3(1):1–21

    Article  Google Scholar 

  15. Bosco GL (2001) A genetic algorithm for image segmentation. In: Proceedings IEEE 11th International Conference on Image Analysis and Processing pp 262–266

  16. Kim EY, Park SH, ad Kim HJ (2000) A genetic algorithmbased segmentation of Markov random field modeled images. IEEE Signal Process Lett 7(11):301–303

    Article  Google Scholar 

  17. Kennedy I, Eberhart RC (1995) Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks pp 1942-1948

  18. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  19. Holland JH (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  20. Cheng H, Sun Y (2000) A hierarchical approach to color image segmentation using homogeneity. IEEE Trans Image Process 9(12):2071–2082

    Article  Google Scholar 

  21. Liu J, Yang YH (1994) Multiresolution color image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 16:689–700

    Article  Google Scholar 

  22. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300/html/dataset/images/color/test-001-025.html

  23. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300/html/dataset/images/color/test-026-050.html

  24. Kumar S, Pant M, Ray AK (2013) A comparison of differential evolution, particle swarm optimization, artificial bee colony and cuckoo search for multilevel thresholding of waste wood. Computer Methods Mater Sci 13(1):135–140

    Google Scholar 

  25. Kumar S, Kumar P, Sharma TK, Pant M (2013) Bi-level thresholding using PSO. Memetic Comp Springer, Artificial Bee Colonyand MRLDE embedded with Otsu method. doi:10.1007/s12293-013-0123-5

    Google Scholar 

  26. Tao Wen-Bing, Tian Jin-Wen, Liu Jian (2003) Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm. Pattern Recogn Lett 24:3069–3078

    Article  Google Scholar 

  27. Hammouche K, Diaf M, Siarry P (2008) A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput Vis Image Underst 109(2008):163–175

    Article  Google Scholar 

  28. Tang Kezong, Yuan Xiaojing, Sun Tingkai, Yang Jingyu, Gao Shang (2011) An improved scheme for minimum cross entropy threshold selection based on genetic algorithm. Knowl-Based Syst 24:1131–1138

    Article  Google Scholar 

  29. Du Feng A, Shi W, Chen L, Deng YA, Zhu Z (2005) Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO). Pattern Recognit Lett 26:597–603

    Article  Google Scholar 

  30. Yin Peng-Yeng (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl Math Comput 184:503–513

    MathSciNet  MATH  Google Scholar 

  31. Linyi L, Deren LB (2008) Fuzzy entropy image segmentation based on particle swarm optimization. Progress Natural Sci 18:1167–1171

    Article  Google Scholar 

  32. Maitra Madhubanti, Chatterjee Amitava (2008) A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst Appl 34:1341–1350

    Article  Google Scholar 

  33. Valentı´n O-E, Erik C, Humberto S (2013) A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Syst Appl 40:1213–1219

    Article  Google Scholar 

  34. Tao Wenbing, Jin Hai, Liu Liman (2007) Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recogn Lett 28:788–796

    Article  Google Scholar 

  35. Chander Akhilesh, Chatterjee Amitava, Siarry Patrick (2011) A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst Appl 38:4998–5004

    Article  Google Scholar 

  36. Akay B (2013) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13:3066–3091

    Article  Google Scholar 

  37. Mohamad F, Nosratallah F, Mohammad T (2010) Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Eng Appl Artif Intell 23:160–168

    Article  Google Scholar 

  38. Zhang Yong, Huang Dan, Ji Min, Xie Fuding (2011) Image segmentation using PSO and PCM with Mahalanobis distance. Expert Syst Appl 38:9036–9040

    Article  Google Scholar 

  39. Wang Lin, Cao Jianfu, Han Chongzhao (2012) Multidimensional particle swarm optimization-based unsupervised planar segmentation algorithm of unorganized point clouds. Pattern Recogn 45:4034–4043

    Article  Google Scholar 

  40. Benaichouche AN, Oulhadj H, Siarry P (2013) Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digit Signal Process 23:1390–1400

    Article  MathSciNet  Google Scholar 

  41. Gao Hao, Kwong Sam, Yang Jijiang, Cao Jingjing (2013) Particle swarm optimization based on intermediate disturbance strategy algorithm and its application in multi-threshold image segmentation. Inf Sci 250:82–112

    Article  MathSciNet  Google Scholar 

  42. Lee Chi-Yu, Leou Jin-Jang, Hsiao Han-Hui (2012) Saliency-directed color image segmentation using modified particle swarm optimization. Sig Process 92:1–18

    Article  Google Scholar 

  43. Pablo M, Roberto U, Di Ferdinando C, Mario G, Stefano C (2013) Automatic hippocampus localization in histological images using Differential Evolution-based deformable models. Pattern Recognit Lett 34:299–307

    Article  Google Scholar 

  44. Das Swagatam, Sil Sudeshna (2010) Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inf Sci 180:1237–1256

    Article  MathSciNet  Google Scholar 

  45. Cuevas Erik, Zaldivar Daniel, Pérez-Cisneros Marco (2010) A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst Appl 37:5265–5271

    Article  Google Scholar 

  46. Shahryar R, Hamid RT (2008) Image thresholding using micro opposition-based differential evolution (Micro-ODE). In: IEEE Congress on Evolutionary Computation-2008, pp 1409–1417

  47. Nakib A, Daachi B, Siarry P (2012) Hybrid differential evolution using low-discrepancy sequences for image segmentation. In: IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), pp 634–640

  48. Erwie Z, Shu-Kai S, Fan B, Du-Ming T (2005) Optimal multi-thresholding using a hybrid optimization approach. Pattern Recogn Lett 26:1082–1095

    Article  Google Scholar 

  49. Ali Musrrat, Ahn Chang Wook, Pant Millie (2014) Multi-level image thresholding by synergetic differential evolution. Appl Soft Comput 17:1–11

    Article  Google Scholar 

  50. Mukesh Saraswat KV, Arya Harish Sharma (2013) Leukocyte segmentation in tissue images using differential evolution algorithm. Swarm Evol Comput 11:46–54

    Article  Google Scholar 

  51. Soham Sarkar and Swagatam Das (2013) Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—a differential evolution approach. IEEE Trans Image Process 22:4788–4797

    Article  MathSciNet  MATH  Google Scholar 

  52. Ahmed MI, Amin MA, Poon B, Yan H (2014) Retina based biometric authentication using phase congruency. Int. J. Mach. Learn. Cyber 5:933–945

    Article  Google Scholar 

  53. Xu X, Liang J, Lv S, Wu Q (2014) Human facial expression analysis based on image granule LPP. Int. J. Mach. Learn. Cyber. 5:907–921

    Article  Google Scholar 

  54. Tong DL, Mintram R (2010) Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Machine Learn Cybernetics 1(1–4):75–87

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida, India

    Sushil Kumar & Manoj Kumar

  2. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  3. JIIT, Noida, India

    Aditya Dutt

Authors
  1. Sushil Kumar
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  2. Millie Pant
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  3. Manoj Kumar
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  4. Aditya Dutt
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Correspondence to Sushil Kumar.

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Kumar, S., Pant, M., Kumar, M. et al. Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms. Int. J. Mach. Learn. & Cyber. 9, 163–183 (2018). https://doi.org/10.1007/s13042-015-0360-7

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  • DOI: https://doi.org/10.1007/s13042-015-0360-7

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