Recent Applications of Swarm-Based Algorithms to Color Quantization

Nowadays, images are very important elements in everyday communication. Current devices include high-quality displays that allow showing images with many colors. Nevertheless, the size of these images is a major issue when the speed of transmission and the storage space must be taken into consideration. The color quantization process reduces the number of different colors used to represent an image while trying to make the new image similar to the original. In addition to enabling visualization with low-end devices and efficient image storage and transmission, color reduction is related to other operations applied to images, such as segmentation, compression, texture analysis, watermarking and content-based image retrieval. The color quantization problem is complex, since the selection of the best colors to represent the image is a NP-complete problem. The complexity and interest of the problem have led to several solution approaches over the years. Recently, several interesting solutions have been proposed that apply swarm-based algorithms. Said algorithms are based on a population of individuals that cooperate to solve a problem. This chapter focuses on the swarm-based solutions proposed for the color quantization problem and shows that these novel methods can generate better images than those obtained by classical solution approaches.

[1]  Dexian Zhang,et al.  A Swarm Intelligence Based Color Image Quantization Algorithm , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[2]  Harish Sharma,et al.  Memetic search in artificial bee colony algorithm , 2013, Soft Computing.

[3]  Adel Nadjaran Toosi,et al.  Color Quantization Using Modified Artificial Fish Swarm Algorithm , 2011, Australasian Conference on Artificial Intelligence.

[4]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[5]  María-Luisa Pérez-Delgado,et al.  A two-stage method to improve the quality of quantized images , 2018, Journal of Real-Time Image Processing.

[6]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[7]  María Luisa Pérez-Delgado.  Artificial ants and fireflies can perform colour quantisation , 2018, Appl. Soft Comput..

[8]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[9]  R. L. Dua,et al.  Fast color image quantization based on bacterial foraging optimization , 2011, ARTCom 2011.

[10]  Xin-She Yang,et al.  Firefly Algorithm: Recent Advances and Applications , 2013, ArXiv.

[11]  Janez Brest,et al.  Memetic artificial bee colony algorithm for large-scale global optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[12]  Shaimaa Ahmed El-Said,et al.  Image quantization using improved artificial fish swarm algorithm , 2015, Soft Comput..

[13]  Abul Hasnat,et al.  Color image quantization using Gaussian Particle Swarm Optimization(CIQ-GPSO) , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[14]  Gilles Venturini,et al.  AntTree: a new model for clustering with artificial ants , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[15]  Hideaki Kawano,et al.  A Color Quantization Based on Vector Error Diffusion and Particle Swarm Optimization Considering Human Visibility , 2015, PSIVT.

[16]  Dorothy Ndedi Monekosso,et al.  A review of ant algorithms , 2009, Expert Syst. Appl..

[17]  Li Xiao-lei,et al.  Applications of artificial fish school algorithm in combinatorial optimization problems , 2004 .

[18]  Dharminder Kumar,et al.  Image Quantization using HSI based on Bacteria Foraging Optimization , 2012 .

[19]  María Luisa Pérez-Delgado.  An iterative method to improve the results of ant-tree algorithm applied to colour quantisation , 2018, Int. J. Bio Inspired Comput..

[20]  Saman Haratizadeh,et al.  Color quantization with clustering by F-PSO-GA , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[21]  María Luisa Pérez-Delgado.  The color quantization problem solved by swarm-based operations , 2018, Applied Intelligence.

[22]  Julia Handl,et al.  Improved Ant-Based Clustering and Sorting , 2002, PPSN.

[23]  Paul S. Heckbert.  Color image quantization for frame buffer display , 1982, SIGGRAPH.

[24]  M. Emre Celebi,et al.  Effective Colour Reduction Using Grey Wolf Optimisation , 2017 .

[25]  Bunyarit Uyyanonvara,et al.  A Hybrid Approach for Color Image Quantization Using K-means and Firefly Algorithms , 2013 .

[26]  Michael N. Vrahatis,et al.  Memetic particle swarm optimization , 2007, Ann. Oper. Res..

[27]  María Luisa Pérez-Delgado,et al.  Color image quantization using the shuffled-frog leaping algorithm , 2019, Eng. Appl. Artif. Intell..

[28]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[29]  Wang Hui,et al.  A Color Clustering Algorithm for Cloth Image , 2008, 2008 IEEE Asia-Pacific Services Computing Conference.

[30]  Janez Brest,et al.  Memetic Self-Adaptive Firefly Algorithm , 2013 .

[31]  Ehsanollah Kabir,et al.  Color reduction based on ant colony , 2007, Pattern Recognit. Lett..

[32]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[33]  Andrés Iglesias,et al.  New memetic self-adaptive firefly algorithm for continuous optimisation , 2016 .

[34]  Iztok Fister,et al.  Memetic firefly algorithm for combinatorial optimization , 2012, 1204.5165.

[35]  María Luisa Pérez-Delgado,et al.  Colour quantization with Ant-tree , 2015, Appl. Soft Comput..

[36]  Michael T. Orchard,et al.  Color quantization of images , 1991, IEEE Trans. Signal Process..

[37]  Kay Chen Tan,et al.  A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  Shengxiang Yang,et al.  A memetic particle swarm optimization algorithm for multimodal optimization problems , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[39]  Dervis Karaboga,et al.  Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm , 2014, Informatica.

[40]  P. Sandhu,et al.  Color Reduction in RGB based on Bacteria Foraging Optimization , 2012 .

[41]  Xiaolin Wu,et al.  EFFICIENT STATISTICAL COMPUTATIONS FOR OPTIMAL COLOR QUANTIZATION , 1991 .

[42]  Michael Gervautz,et al.  A simple method for color quantization: octree quantization , 1990 .

[43]  Michael Randolph Garey,et al.  The complexity of the generalized Lloyd - Max problem , 1982, IEEE Trans. Inf. Theory.

[44]  Anthony H. Dekker,et al.  Kohonen neural networks for optimal colour quantization , 1994 .

[45]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[46]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[47]  Baldo Faieta,et al.  Diversity and adaptation in populations of clustering ants , 1994 .

[48]  Ajith Abraham,et al.  Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications , 2009, Foundations of Computational Intelligence.

[49]  Andries Petrus Engelbrecht,et al.  A Color Image Quantization Algorithm Based on Particle Swarm Optimization , 2005, Informatica.

[50]  P. Prusinkiewicz,et al.  Variance‐based color image quantization for frame buffer display , 1990 .

[51]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.