Color quantization with Particle swarm optimization and artificial ants

This article describes a color quantization algorithm that combines two swarm-based methods: Particle swarm optimization and artificial ants. The proposed method is based on a previous method that solves the quantization problem by combining the Particle swarm optimization algorithm with the K-means algorithm. K-means is a popular clustering method that has been applied to solve a variety of problems, including the color quantization problem. Nevertheless, it is a time-consuming method, which makes combining the Particle swarm optimization algorithm and K-means less suitable than other color quantization techniques. The proposed method, however, discards the K-means algorithm and applies the Ant-tree for color quantization algorithm in order to reduce execution time. This article shows that the new method outperforms the original one, since it requires less time to obtain higher quality images. In addition, the images produced are also of better quality than those produced by other well-known color quantization methods, such as Neuquant, Octree, Median-cut, Variance-based, Binary splitting and Wu’s methods.

[1]  Masayuki Okamoto,et al.  Color quantization using the fast K-means algorithm , 2000 .

[2]  Esteban J. Palomo,et al.  Hierarchical Color Quantization Based on Self-organization , 2013, Journal of Mathematical Imaging and Vision.

[3]  Paul Scheunders,et al.  A genetic c-Means clustering algorithm applied to color image quantization , 1997, Pattern Recognit..

[4]  Noriaki Suetake,et al.  Color quantization method based on principal component analysis and linear discriminant analysis for palette-based image generation , 2017 .

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

[6]  Chi-Man Pun,et al.  Color image segmentation using adaptive color quantization and multiresolution texture characterization , 2014, Signal Image Video Process..

[7]  Jing-Yu Yang,et al.  Content-based image retrieval using color difference histogram , 2013, Pattern Recognit..

[8]  M. Emre Celebi,et al.  Effective initialization of k-means for color quantization , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[9]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[10]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[11]  Quan Wen,et al.  Colour quantisation using the adaptive distributing units algorithm , 2014 .

[12]  Thomas E. Potok,et al.  Document clustering using particle swarm optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[13]  José Alfredo Ferreira Costa,et al.  Hierarchical SOM applied to image compression , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[14]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[15]  Sung Wook Baik,et al.  Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems , 2017, Journal of Real-Time Image Processing.

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

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

[18]  Gilles Venturini,et al.  A hierarchical ant based clustering algorithm and its use in three real-world applications , 2007, Eur. J. Oper. Res..

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

[20]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[21]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[22]  Gen-ke Yang,et al.  Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem , 2006 .

[23]  Xiangyang Wang,et al.  An effective image retrieval scheme using color, texture and shape features , 2011, Comput. Stand. Interfaces.

[24]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[25]  Chaur-Heh Hsieh,et al.  Sample-size adaptive self-organization map for color images quantization , 2007, Pattern Recognit. Lett..

[26]  R. J. Kuo,et al.  Integration of particle swarm optimization and genetic algorithm for dynamic clustering , 2012, Inf. Sci..

[27]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[28]  Xin Chen,et al.  A new compression scheme for color-quantized images , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

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

[30]  Bing-Hwang Su,et al.  Accelerated k-means clustering algorithm for colour image quantization , 2008 .

[31]  Gerald Schaefer,et al.  Fuzzy clustering for colour reduction in images , 2009, Telecommun. Syst..

[32]  Quan Wen,et al.  An effective real-time color quantization method based on divisive hierarchical clustering , 2012, Journal of Real-Time Image Processing.

[33]  Xiaolin Wu,et al.  Color quantization by dynamic programming and principal analysis , 1992, TOGS.

[34]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[36]  Wen-Hsiang Tsai,et al.  Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle , 1998, Pattern Recognit. Lett..

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

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

[39]  Lale Akarun,et al.  A fuzzy algorithm for color quantization of images , 2002, Pattern Recognit..

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

[41]  Quan Wen,et al.  Hard versus fuzzy c-means clustering for color quantization , 2011, EURASIP J. Adv. Signal Process..

[42]  M. Emre Celebi,et al.  Improving the performance of k-means for color quantization , 2011, Image Vis. Comput..

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

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

[45]  Abdesselam Bouzerdoum,et al.  Skin segmentation using color pixel classification: analysis and comparison , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  A. Engelbrecht,et al.  A new locally convergent particle swarm optimiser , 2002, IEEE International Conference on Systems, Man and Cybernetics.

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

[48]  Chip-Hong Chang,et al.  New adaptive color quantization method based on self-organizing maps , 2005, IEEE Transactions on Neural Networks.

[49]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[50]  Charalambos Strouthopoulos,et al.  Adaptive color reduction , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[51]  Imtiaz Ahmad,et al.  Particle swarm optimization for task assignment problem , 2002, Microprocess. Microsystems.

[52]  Chu-Sing Yang,et al.  A fast particle swarm optimization for clustering , 2015, Soft Comput..

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

[54]  Patricio A. Vela,et al.  A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..

[55]  Aderemi Oluyinka Adewumi,et al.  On the performance of particle swarm optimisation with(out) some control parameters for global optimisation , 2016, Int. J. Bio Inspired Comput..

[56]  Mehmet Fatih Tasgetiren,et al.  A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem , 2008, Comput. Oper. Res..