Interactive Evolutionary Colour Assignment and Proportioning for Camouflage Design with K-Means and Genetic Algorithm: A Case Study of Nigeria’s Landscape

Document Type : Original Article


1 Department of Fashion Design, Kyungpook National University, Sangju 37224, Republic of Korea

2 Department of Robot and Smart System Engineering, Kyungpook National University, Daegu 41566, Republic of Korea


Natural camouflage, seamlessly blending animals with their surroundings, remains challenging for artificial counterparts. Some animals exhibit near-permanent camouflaging, a product of decades of genetic evolution with their environment. At the same time, chameleons and octopuses achieve the ideal desired instantaneous camouflaging, unlike the heuristic-based approach of the artificial camouflage design. To attain similar perfection seen in animals, an evolutionary approach to artificial camouflage pattern development is necessary. Developing nations, primarily adopting the camouflage patterns of their more developed counterparts, may find themselves at a disadvantage. This study proposes a Genetic Algorithm (GA)-based approach to aid designers in developing countries in crafting effective camouflage. By parameterising heuristic development as a procedural texturing problem and evolving colour assignments iteratively, this approach aims to emulate the evolutionary process seen in nature. Using the K-means algorithm, genes are initialised based on background image colours, exploring factorial combinations to achieve optimal camouflage. With a maximum of 100 iterations and interactive feedback, the method addresses Nigeria's specific case and offers a faster development solution than developed nations' approaches. This evolutionary approach could revolutionise artificial camouflage development worldwide.


Main Subjects

  1. Lin CJ, Chang CC, Lee YH. Evaluating camouflage design using eye movement data. Appl Ergon. 2014; 45: 714-23. 09. 012.
  2. Stevens M, Merilaita S. Defining disruptive coloration and distinguishing its functions. Philos Trans R Soc B Biol Sci. 2008;364:481–8. RSTB. 2008.0216.
  3.                 WALLACE AR. The Colours of Animals. Nat. 1890 421082 1890; 42:289-91. 042289a0.
  4. Camouflage-Wikipedia 2022. wiki/Camouflage (accessed November 30, 2022).
  5. Xiao H, Qu Z, Lv M, Jiang Y, Wang C, Qin R. Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning. Appl Sci. 2020, Vol 10, 
    Page 5284 2020;10:5284. APP10155284.
  6. Larson EH. Camouflage: Modern International Military Patterns. Pen \& Sword Books Limited; 2022.
  7. Borsarello JF, Palinckx W. Camouflage Uniforms of Asian and Middle Eastern Armies. Schiffer Publishing, Limited; 2004.
  8. Jong Seok L. 한국자연환경과융합한위장패턴디자인연구. 한국과학예술융합학회 2022; 40:305-17. KSAF.2022.12.30.305.
  9. Mutlag WK, Ali SK, Aydam ZM, Taher BH. Feature Extraction Methods: A Review. J Phys Conf Ser. 2020;1591:012028.
  10. Zhou X, Gong Y, Meng X-M, Hasan H, Shafri HZ, et al. Extraction of High-level and Low-level feature for classification of Image using Ridgelet and CNN based Image Classification. J Phys Conf Ser. 2021; 1911:012019. 1911/ 1/012019.
  11. Goñi SM, Salvadori VO. Color measurement: comparison of colorimeter vs. computer vision system. J Food Meas Charact. 2017;11:538-47. https:// 8.
  12. Senniappan Karuppusamy N, Kang BY. Minimizing airtime by optimizing tool path in computer numerical control machine tools with application of A* and genetic algorithms. Adv Mech Eng. 2017;9. JPEG.
  13. Lee J, Kang BY, Kim DW. Fast genetic algorithm for robot path planning. Electron Lett. 2013;49:1449-51.
  14. Reynolds C. Interactive Evolution of Camouflage. Artif Life 2011;17:123–36. ARTL_A_00023.
  15. Perlin K. An image synthesizer. ACM SIGGRAPH Comput Graph. 1985;19:287-96. 1145/ 325165.325247.
  16. Lagae A, Dutré P. A Comparison of Methods for Generating Poisson Disk Distributions. Comput Graph Forum. 2008;27:114-29. 1467- 8659.2007.01100.X.
  17. Gagné C, Parizeau M. Genericity in evolutionary computation software tools: principles and case study. Int J Artif Intell Tools. 2006; 15:173-94.
  18. Chen K-T. It’s a wrap! visualisations that wrap around cylindrical, toroidal, or spherical topologies 2022.
  19. Borrelli V, Jabrane S, Lazarus F, Thibert B. Flat tori in three-dimensional space and convex integration. Proc Natl Acad Sci USA. 2012; 109:7218-23. ASSETS/GRAPHIC/PNAS.1118478109EQ63.GIF.
  20. Nwachukwu CE, Olufunmilayo EO, Chiroma GB, Okoye CF. Perception of National Youth Service Corps (NYSC) among corps medical doctors in Nigeria: a cross-sectional study. BMC Med Educ. 2023; 23:1-9.
  21. Marenin O. Implementing deployment policies in 
    the national youth service corps of nigeria. Http://Dx DoiOrg/101177/0010414090022004002 1990;22:397-436. 0010414090022004002.
  22. Community BO. Blender - a 3D modelling and rendering package. Blender Found 2018. (accessed October 6, 2023).
  23. Gad AF. PyGAD: An Intuitive Genetic Algorithm Python Library. ArXiv 2021;abs/2106.0.
  24. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in {P}ython. J Mach Learn Res 2011; 12:2825-30.
  25. Bradski G. The OpenCV Library. Dr Dobb’s J Softw Tools 2000.
  26. Grinberg M. Flask web development: developing web applications with python. “O’Reilly Media, Inc.”; 2018.