Near real-time Evolution-based Adaptation Strategy for dynamic Color Quantization of image sequences

Information sciences

Volumn 122, Issue 2, 2000, Pages 161-183

  Gonzalez A I ^a   Grana M ^a   Albizuri, F X ^a   D'Anjou, A ^a   Torrealdea, F J ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; IMAGE ANALYSIS; MATRIX ALGEBRA; REAL TIME SYSTEMS; VECTOR QUANTIZATION;

COLOR QUANTIZATION; NON-STATIONARY CLUSTERING;

COLOR IMAGE PROCESSING;

EID: 0342647294     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(99)00119-X     Document Type: Article

References (44)

1. Aizawa A.N., Wah B.W. Scheduling of genetic algorithms in a noisy environment. Evolutionary Computation. 2(2):1994;97-122.
2. C. Alippi, R. Cucchiara, Cluster partitioning in image analysis classification: a genetic algorithm approach, Proceedings of the IEEE COMPEURO, The Hague, 1992, pp. 139-144.
3. Andrey P., Tarroux P. Unsupervised image segmentation using a distributed genetic algorithm. Pattern Recognition. 27(5):1994;659-673.
4. Babu G.P., Murty N.M. Clustering with evolution strategies. Pattern Recognition. 27(2):1994;321-329.
5. Back T., Schwefel H.P. An overview of evolution algorithms for parameter optimization. Evolutionary Computation. 1:1993;1-24.
6. Back T., Schwefel H.P. Evolutionary computation: an overview. IEEE ICEC'96. 1996;20-29.
7. +λ) theory Evolutionary Computation. 1(2):1993;165-188.
8. Beyer H.G. Toward a theory of evolution strategies: the. (μ,λ) theory Evolutionary Computation. 2(4):1994;381-407.
9. Beyer H.G. Toward a theory of evolution strategies: on the benefits of sex - the. (μ/μ,λ) theory Evolutionary Computation. 3(1):1995a;81-111.
10. Beyer H.G. Toward a theory of evolution strategies: self-adaptation. Evolutionary Computation. 3(3):1995b;311-347.
11. J.C. Bezdek, S. Boggavaparu, L.O. Hall, A. Bensaid, Genetic algorithm guided clustering, in: Proceedings of the First IEEE Conference Evol. Comp., 1994, pp. 34-39.
12. J.C. Bezdek, R.J. Hathaway, Optimization of fuzzy clustering criteria using genetic algorithms, in: Proceedings of the First IEEE Conference Evol. Comp., IEEE press, 1994, pp. 589-594.
13. J.N. Bhuyan, V.V. Raghavan, V.K. Elayavalli, Genetic algorithm for clustering with an ordered representation, in: Proceedings of the Fourth International Cong. Gen. Alg., 1991, pp. 408-415.
14. K. Blekas, A. Stafylopatis, Real coded genetic optimization of fuzzy clustering, EUFIT-96, Aachen, Germany, 1996.
15. B.P. Buckles, F.E. Petry, D. Prabhu, R. George, R. Srikanth, Fuzzy clustering with genetic search, in: Proceedings of the First IEEE Conf. Evol. Comp., 1994, pp. 46-50.
16. Chaudhuri B.B. Dynamic clustering for time incremental data. Patern Recog. Lett. 15(1):1994;27-34.
17. Chen H.H., Chen Y.S., Hsu W.H. Low rate sequence image coding via vector quantization. Signal Processing. 26:1995;265-283.
18. Chen O.T., Chen B.J., Zhang Z. An adaptive vector quantization based on the gold-washing method for image compression. IEEE Trans. Circuits Systems for Video Techn. 4(2):1994;143-156.
19. Chen R.J., Chien B.C. Three-dimensional morphological pyramid and its application to color image sequence coding. Signal Processing. 44:1995;163-180.
20. Diday E., Simon J.C. Clustering analysis. Fu K.S. Digital Pattern Recognition. 1980;47-94 Springer, Berlin.
21. Duda R.D., Hart P.E. Pattern Classification and Scene Analysis. 1973;Wiley, New York.
22. Fitzpatrick J.M., Grefenstette J.J. Genetic algorithms in noisy environments. Machine Learning. 3:1988;101-120.
23. J.E. Fowler, A survey of adaptive vector quantization - Part I: A unifying structure, IEEE Transactions on Communications, submitted.
24. Fukunaga K. Statistical Pattern Recognition. 1990;Academic Press, New York.
25. Garcia J.A., Fdez-Valdivia J., Cortijo F.J., Molina R. A dynamic approach for clustering data. Signal Processing. 44(2):1995;181-196.
26. A. Gersho, R.M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publisher, New York, 1992.
27. Gong Y., Zen H., Ohsawa Y., Sakauchi M. A color video image quantization method with stable and efficient color selection capability. Int. Conf. Pattern Recog. 3:1992;33-36.
28. Gonzalez A.I., Graña M., Lozano J.A., Larrañaga P. Experimental results of a Michigan-like evolution strategy for non-stationary clustering. Smith G.D., Steele N.C., Albrecht R.F. Artificial Neural Nets and Genetic Algorithms. 1998;555-559 Springer, Berlin.
29. Hartigan J. Clustering Algorithms. 1975;Wiley, New York.
30. Heckbert P. Color image quantization for frame-buffer display. Computer Graphics. 16(3):1980;297-307.
31. Jain A.K., Dubes R.C. Algorithms for clustering data. 1988;Prentice-Hall, Englewood Cliffs, NJ.
32. D.R. Jones, M.A. Beltrano, Clustering with genetic algorithms, Operating Sciences Dept., Generla Motors Res. Lab., Warren, Mich. 1990.
33. Kankanhalli M.S., Mehtre B.M., Wu J.K. Cluster based color matching for image retrieval. Pattern Recognition. 29(4):1996;701-708.
34. F.Z. Ketaff, J.P. Asselin de Beauville, Genetic and fuzzy based clustering, in: Proceedings of the 5th Conf. Int. Fed. Class. Soc. Kobe, Japan, 1996, pp. 100-103.
35. Lin T.S., Chang L.W. Fast color image quantization with error diffusion and morphological operations. Signal Processing. 43:1995;293-303.
36. Lucasius C.B., Dane A.D., Kateman G. On k-medoid clustering of large data sets with the aid of a genetic algorithm: background feasibility and comparison. Analytica Chimica Acta. 282:1993;647-669.
37. S. Luchian, H. Luchian, M. Petriuc, Evolutionary automated classification, in: Proceedings of the 1st IEEE Conf. Evol. Comp., 1994, pp. 585-588.
38. Michalewicz Z. Evolutionary computation: practical issues. IEEE ICEC'96. 1996;30-39.
39. Moraczewski I.R., Borkowski W., Kierzek A. Clustering geobotanical data with the use of a genetic algorithm. COENOSES. 10(1):1995;17-28.
40. Orchard M.T., Bouman C.A. Color quantization of images. IEEE Trans. Signal Processing. 39(12):1991;2677-2690.
41. Ostermeier A., Gawelczyk A., Hansen N. A derandomized approach to self-adaptation of evolution strategies. Evolutionary Computation. 2(4):1995;369-380.
42. Uchiyama T., Arbib M.A. Color image segmentation using competitive learning. IEEE . Patt. Anal. and Mach. Int. 16(12):1994;1197-1206.
43. X. Wu, Efficient statistical computations for optimal color quantization, in: J. Arvo (Ed.), Graphics Gems II, Academic Press Professional, 1991, pp.126-133.
44. Yin G., Rudolph G., Schwefel H.P. Analyzing the. (1,λ) evolution strategy via stochastic approximation methods Evolutionary Computation. 3(4):1995;473-490.