An incremental-encoding evolutionary algorithm for color reduction in images

Integrated Computer-Aided Engineering

Volumn 17, Issue 3, 2010, Pages 261-269

  Carro Calvo, Leo ^a   Salcedo Sanz, Sancho ^a   Ortiz García, Emilio G ^a   Portilla Figueras, Antonio ^a  

^a UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Color reduction; dithering; evolutionary algorithms; incremental encoding

Indexed keywords

COLOR QUANTIZATION; COLOR REDUCTION; DITHERING; EVOLUTIONARY COMPUTATION TECHNIQUES; IMAGE QUANTIZATION; INCREMENTAL-ENCODING; NATURE-INSPIRED ALGORITHMS; PRE-PROCESSING STEP; RGB IMAGES;

COLOR; ENCODING (SYMBOLS); IMAGE CODING; IMAGE SEGMENTATION; NEURAL NETWORKS;

EVOLUTIONARY ALGORITHMS;

EID: 78649560423     PISSN: 10692509     EISSN: None     Source Type: Journal    
DOI: 10.3233/ICA-2010-0343     Document Type: Conference Paper

References (35)

1. A. Atsalakis and N. Papamarkos, Color reduction and estima-tion of the number ofdominant colors by using a self-growing and self-organized neural GAs, Engineering Applications of Artificial Intelligence 19(7) (2006), 769-786.
2. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Func-tionAlgorithms, Plenumpress, 1981.
3. Z. Bing, S. Junyi and P. Qinke, An adjustable algorithm for col-or quantization, Pattern Recognition Letters 25 (2004), 1787-1797.
4. B. Cyganek, Circular Road Signs Recognition with Soft Clas-sifiers, Integrated Computer-AidedEngineering 14(4) (2007), 323-343.
5. A.H. Dekker, Kohonen neural networks for optimal color quantization, Network: Computation in Neural Systems 5 (1994), 351-367.
6. K.A. DeJong and J. Sarma, Generation Gaps Revisited, In Proc. Of Foundations of Genetic Algorithms 2, pp. 19-28, Morgan Kaufmann, 1993.
7. A.E. Eiben and J.E. Smith, Introduction toEvolutionary Com-puting, Springer-Verlag, 2003.
8. P. Fränti, J. Kivijarvi, T. Kaukoranta and O. Nevalainen, Ge-netic algorithms for large scale clustering problem, The Com-puterJournal 40(9) (1997), 547-554.
9. P. Fränti, Genetic algorithm with deterministic crossover for vectorquantization, PatternRecognitionLetters21(1) (1997), 61-68
10. P. Fränti and O. Virmajoki, Iterative shrinking method for clustering problems, PatternRecognition 39 (2006), 761-775.
11. M. Gervautz and W. Purgathofer, A SimpleMethod for Color Quantization: Octree Quantization, In Graphics Gems, A. S. Glassner, Ed., New York: Academic, 1990, pp. 287-293.
12. A.T. Ghanbarian, E. Kabirand N.M. Charkari, Color reduc-tion based on ant colony, Pattern Recognition Letters 28(12) (2007), 1383-1390.
13. A.B. Hamza, Y. He, H. Krim and A. Willsky, A multiscale approach to pixel-level image fusion, Integrated Computer AidedEngineering 12(2) (2005), 135-146.
14. P. Heckbert, Color image quantization for frame buffer display, Computers & Graphics 16 (1982), 297-307.
15. H. Huang, Y. Chen and W. Hsu, Color image segmentation using a self-organized map algorithm, Journal of Electronic Imaging 2 (2002), 136-148.
16. I. Jafar and H. Ying, New algorithms for contrast enhancement in grayscale images based onthe variational definition ofhis-togram equalization, Integrated Computer-AidedEngineering 15(2)(2008), 131-147.
17. K. Kanjanawanishkula and B. Uyyanonvarab, Novel fast color reduction algorithm fortime-constrained applications, Journal ofVisual Communication & Image Representation 16 (2005), 311-332.
18. J. Kivijarvi, P. Fränti and O. Nevalainen, Self-adaptive genetic algorithm for clustering, Journal ofHeuristics 9(2) (2003), 113-129.
19. T. Kohonen, The self-organizing map, Proceedings of the IEEE78(9) (1990), 1464-1480.
20. H. Lee, E. Kim and M. Park, A genetic feature weighting scheme for pattern recognition, Integrated Computer Aided Engineering 14(2) (2007), 161-171.
21. Y. Lim and S. Lee, Onthe color image segmentation algorithm based on the thresholding and the fuzzy C-means techniques, Pattern Recognition 23(9) (1990), 935-952.
22. E. López-Rubio, J. Muñoz-Pérez and J.A. Gómez-Ruiz, A four-stage system for blind colour image segmentation, Inte-gratedComputerAided Engineering 10(2)(2003), 127-137.
23. Ohio State University Signal Analysis and Machine Percep-tion Laboratory, Repository ofImages, http://sampl.eng.ohio-state.edu.
24. N. Papamarkos, Color reduction using local features and a KSOFM neural network, International Journal ofImaging Systems and Technology 10 (2003), 404-409.
25. N. Papamarkos, A. Atsalakis and C. Strouthopoulos, Adap-tive color reduction, IEEE Transactions on Systems, Man and Cybernetics PartB: Cybernetics 32(1) (2002), 44-56.
26. N. Papamarkos and B. Gatos, A new approach for multithresh-old selection, Computer Vision Graphs and Image Processing 10(5) (1999), 404-409.
27. H. Yin and I. Hussain, Independent Component Analysis and Non-Gaussianity for Blind Image Deconvolution Deblurring, Integrated Computer-AidedEngineering 15(3) (2008), 219-288.
28. Y.H. Said, On Genetic Algorithms and their Applications, Handbook ofStatistics 24 (2005), 359-390.
29. K.C. Sarma and H. Adeli, Bi-level parallel genetic algorithms for optimizationoflarge steel structures, Computer-AidedCiv-il andInfrastructureEngineering 16(5) (2001), 295-304.
30. P. Scheunders, A comparison ofclustering algorithms for color image quantization, Pattern Recognition Letters 18 (1997), 1379-1384.
31. O. Verevka, The Local K-means Algorithm for Color Image Quantization, M.Sc. dissertation, University of Alberta, Ed-monton, AB, Canada.
32. S.J. Wan, P. Prusinkiewicz and S.K. Wong, 'Variance based color image quantization for frame bufier display, Color Re-search andApplications 15(1) (1990), 52-58.
33. R. Wehrens and L. Buydens, Evolutionary optimisation: a tutorial, TrAC Trends in Analytical Chemistry 17(4) (1998), 193-203.
34. X. Wu, Color quantization by dynamic programming and prin-cipal analysis, ACM Transactions on Graphics 11(4) (1998), 384-392.
35. K. KrishnaandM.N. Murty, Genetick-meansalgorithm, IEEE Transactions on Systems, Man and Cybernetics 29(3) (1998), 433-439