Computational intelligence in multimedia processing: Foundation and trends

Studies in Computational Intelligence

Volumn 96, Issue , 2008, Pages 3-49

  Hassanien, Aboul Ella ^a,b   Abraham, Ajith ^c   Kacprzyk, Janusz ^d   Peters, James F ^e  

^a CAIRO UNIVERSITY   (Egypt)

^b KUWAIT UNIVERSITY   (Kuwait)

^c NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^d SYSTEMS RESEARCH INSTITUTE   (Poland)

^e UNIVERSITY OF MANITOBA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77952251579     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-76827-2_1     Document Type: Article

References (117)

1. Abraham A., Jain R., Thomas J., and Han S. Y. (2007) D-SCIDS: Distributed soft computing intrusion detection systems. Journal of Network and Computer Applications, vol. 30, no. 1, pp. 81-98.
2. Bishop C. M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, Oxford.
3. Kohonen T. (1988) Self-Organization and Associative Memory. Springer, Berlin Heidelberg New York.
4. Carpenter G. and Grossberg S. (1995) Adaptive Resonance Theory (ART). In: Arbib M. A. (ed.), The Handbook of Brain Theory and Neural Networks. MIT, Cambridge, pp. 79-82.
5. Grossberg S. (1976) Adaptive pattern classification and universal recoding: Parallel development and coding of neural feature detectors. Biological Cybernetics, vol. 23, pp. 121-134.
6. Abraham A. (2001) Neuro-fuzzy systems: State-of-the-art modeling techniques, connectionist models of neurons, learning processes, and artificial intelligence. In: Jose Mira and Alberto Prieto (eds.), Lecture Notes in Computer Science, vol. 2084, Springer, Berlin Heidelberg New York, pp. 269-276.
7. Nguyen H. T. and Walker E. A. (1999) A First Course in Fuzzy Logic. CRC, Boca Raton.
8. Hassanien A. E. and Jafar Ali (2003) Image classification and retrieval algorithm based on rough set theory. South African Computer Journal (SACJ), vol. 30, pp. 9-16.
9. Hassanien A. E. (2006) Hiding iris data for authentication of digital images using wavelet theory. International journal of Pattern Recognition and Image Analysis, vol. 16, no. 4, pp. 637-643.
10. Hassanien A. E., Ali J. M., and Hajime N. (2004) Detection of spiculated masses in Mammograms based on fuzzy image processing. In: 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC2004, Zakopane, Poland, 7-11 June. Lecture Notes in Artificial Intelligence, vol. 3070. Springer, Berlin Heidelberg New York, pp. 1002-1007.
11. Fogel L. J., Owens A. J., and Walsh M. J. (1967) Artificial Intelligence Through Simulated Evolution. Wiley, New York.
12. Fogel D. B. (1999) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2nd edition. IEEE, Piscataway, NJ.
13. Pearl J. (1997) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco.
14. Dubois D., Prade H., and Sèdes F. (2001) Fuzzy logic techniques in multimedia database querying: A preliminary investigation of the potentials. IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 3, pp. 383-392.
15. Holland J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor.
16. Goldberg D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading.
17. Zlokolica V., Piurica A., Philips W., Schulte S., and Kerre E. (2006) Fuzzy logic recursive motion detection and denoising of video sequences. Journal of Electronic Imaging, vol. 15, no. 2.
18. Koza J. R. (1992) Genetic Programming. MIT, Cambridge, MA.
19. Buscicchio C. A., Grecki P., and Caponetti L. (2006) Speech emotion recognition using spiking neural networks. In: Esposito F., Ras Z. W., Malerba D., and Semeraro G. (eds.), Foundations of Intelligent Systems, Lecture Notes in Computer Science, vol. 4203, Springer, Berlin Heidelberg New York, pp. 38-46.
20. Ford R. M. (2005) Fuzzy logic methods for video shot boundary detection and classification. In: Tan Y.-P., Yap K. H., and Wang L. (eds.) Intelligent Multimedia Processing with Soft Computing, Studies in Fuzziness and Soft Computing, vol. 168, Springer, Berlin Heidelberg New York, pp. 151-169.
21. Back T. (1996) Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York.
22. Ming Li and Tong Wang (2005) An approach to image retrieval based on concept lattices and rough set theory. Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, 5-8 Dec., pp. 845-849.
23. Kulkarni S. (2004) Neural-fuzzy approach for content-based retrieval of digital video. Canadian Conference on Electrical and Computer Engineering, vol. 4, 2-5 May, pp. 2235-2238.
24. Hui Fang, Jianmin Jiang, and Yue Feng (2006) A fuzzy logic approach for detection of video shot boundaries. Pattern Recognition, vol. 39, no. 11, pp. 2092-2100.
25. Selouani S.-A. and O'Shaughnessy D. (2003) On the use of evolutionary algorithms to improve the robustness of continuous speech recognition systems in adverse conditions. EURASIP Journal on Applied Signal Processing, vol. 8, pp. 814-823
26. Lo C.-C. and Wang S.-J. (2001) Video segmentation using a histogram-based fuzzy C-means clustering algorithm. The 10th IEEE International Conference on Fuzzy Systems, vol. 2, 2-5 Dec., pp. 920-923.
27. Cao X. and Suganthan P. N. (2002) Neural network based temporal video segmentation. International Journal of Neural Systems, vol. 12, no. 3-4, pp. 263-629.
28. Chang C.-H., Ye Z., and Zhang M. (2005) Fuzzy-ART based adaptive digital watermarking scheme. IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, no. 1, pp. 65-81.
29. Diego Sal Diaz and Manuel Grana Romay (2005) Introducing a watermarking with a multi-objective genetic algorithm. Proceedings of the 2005 conference on Genetic and evolutionary computation, Washington DC, USA, pp. 2219-2220.
30. Lou D.-C. and Yin T.-L. (2001) Digital watermarking using fuzzy clustering technique. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japan), vol. E84-A, no. 8, pp. 2052-2060.
31. Maher El-arbi, Ben Amar, and C. Nicolas, H. (2006) Video watermarking based on neural networks. IEEE International Conference on Multimedia and Expo, Toronto, Canada, pp. 1577-1580.
32. Der-Chyuan Lou, Jieh-Ming Shieh, and Hao-Kuan Tso (2005) Copyright protection scheme based on chaos and secret sharing techniques. Optical Engineering, vol. 44, no. 11, pp. 117004-117010.
33. Wei Lu, Hongtao Lu, and FuLai Chung (2005) Subsampling-based robust watermarking using neural network detector. Advances in Neural Networks, ISNN 2005, Lecture Notes in Computer Science, vol. 3497, pp. 801-806.
34. Cheng-Ri Piao, Sehyeong Cho, and Seung-Soo Han (2006) Color image watermarking algorithm using BPN neural networks. Neural Information Processing, Lecture Notes in Computer Science, vol. 4234, pp. 234-242
35. Zheng Liu, Xue Li, and Dong Z. (2004) Multimedia authentication with sensorbased watermarking. Proc. of the international workshop on Multimedia and security, Magdeburg, Germany, pp. 155-159
36. Hung-Hsu Tsai, Ji-Shiung Cheng, and Pao-Ta Yu (2003) Audio watermarking based on HAS and neural networks in DCT domain. EURASIP Journal on Applied Signal Processing, vol. 2003, no. 3, pp. 252-263
37. Cao L., Wang X., Wang Z., and Bai S. (2005) Neural network based audio watermarking algorithm. In: ICMIT 2005: Information Systems and Signal Processing, Wei Y., Chong K. T., Takahashi T. (eds.), Proceedings of the SPIE, vol. 6041, pp. 175-179
38. Alessandro Bugatti, Alessandra Flammini, and Pierangelo Migliorati (2002) Audio classification in speech and music: A comparison between a statistical and a neural approach. EURASIP Journal on Applied Signal Processing, vol. 2002, no. 4, pp. 372-378.
39. Lim Ee Hui, Seng K. P., and Tse K. M. (2004) RBF Neural network mouth tracking for audio-visual speech recognition system. IEEE Region 10 Conference TENCON2004, 21-24 Nov., pp. 84-87.
40. Jian Zhou, Guoyin Wang, Yong Yang, and Peijun Chen (2006) Speech emotion recognition based on rough set and SVM. 5th IEEE International Conference on Cognitive Informatics ICCI 2006, 17-19 July, vol. 1, pp. 53-61.
41. Faraj M.-I. and Bigun J. (2007) Audio-visual person authentication using lipmotion from orientation maps. Pattern Recognition Letters, vol. 28, no. 11, pp. 1368-1382.
42. Halavati R., Shouraki S. B., Eshraghi M., Alemzadeh M., and Ziaie P. (2004) A novel fuzzy approach to speech recognition. Hybrid Intelligent Systems. Fourth International Conference on Hybrid Intelligent Systems, 5-8 Dec., pp. 340-345.
43. Eugene I. Bovbel and Dzmitry V. Tsishkou (2000) Belarussian speech recognition using genetic algorithms. Third International Workshop on Text, Speech and Dialogue, Brno, Czech Republic, pp. 185-204.
44. Fellenz W. A., Taylor J. G., Cowie R., Douglas-Cowie E., Piat F., Kollias S., Orovas C., and Apolloni B. (2000) On emotion recognition of faces and of speech using neural networks, fuzzy logic and the ASSESS system. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 2, IJCNN 2000, pp. 93-98.
45. Laaksonen J., Koskela M., and Oja E. (2002) PicSOM-Self-organizing image retrieval with MPEG-7 content descriptions. IEEE Transactions on Neural Networks, Special Issue on Intelligent Multimedia Processing vol. 13, no. 4, pp. 841-853.
46. Mats S., Jorma L., Matti P., and Timo H. (2006) Retrieval of multimedia objects by combining semantic information from visual and textual descriptors. Proceedings of 16th International Conference on Artificial Neural Networks (ICANN 2006), pp. 75-83, Athens, Greece, September 2006.
47. Kostek B. and Andrzej C. (2001) Employing fuzzy logic and noisy speech for automatic fitting of hearing aid. 142 Meeting of the Acoustical Society of America, No. 5, vol. 110, pp. 2680, Fort Lauderdale, USA.
48. Liu J., Wang Z., and Xiao X. (2007) A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition. Pattern Recognition Letter, vol. 28, No. 8, pp. 912-920.
49. Ing-Jr Ding (2007) Incremental MLLR speaker adaptation by fuzzy logic control. Pattern Recognition, vol. 40, no. 11, pp. 3110-3119
50. Andrzej C. (2003) Automatic identification of sound source position employing neural networks and rough sets. Pattern Recognition Letters, vol. 24, pp. 921-933.
51. Andrzej C., Kostek B., and Henryk S. (2002) Diagnostic system for speech articulation and speech understanding. 144th Meeting of the Acoustical Society of America (First Pan-American/Iberian Meeting on Acoustics), Journal of the Acoustical Society of America, vol. 112, no. 5, Cancun, Mexico.
52. Andrzej C., Andrzej K., and Kostek B. (2003) Intelligent processing of stuttered speech. Journal of Intelligent Information Systems, vol. 21, no. 2, pp. 143-171.
53. Pawel Zwan, Piotr Szczuko, Bozena Kostek, and Andrzej Czyzewski (2007) Automatic singing voice recognition employing neural networks and rough sets. RSEISP 2007, pp. 793-802.
54. Andrzej C. and Marek S. (2002) Pitch estimation enhancement employing neural network-based music prediction. Proc. IASTED Intern. Conference, Artificial Intelligence and Soft Computing, pp. 413-418, Banff, Canada.
55. Hendessi F., Ghayoori A., and Gulliver T. A. (2005) A speech synthesizer for Persian text using a neural network with a smooth ergodic HMM. ACM Transactions on Asian Language Information Processing (TALIP), vol. 4, no. 1, pp. 38-52.
56. Orhan Karaali, Gerald Corrigan, and Ira Gerson (1996) Speech synthesis with neural networks. World Congress on Neural Networks, San Diego, Sept. 1996, pp. 45-50.
57. Corrigan G., Massey N., and Schnurr O. (2000) Transition-based speech synthesis using neural networks. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, pp. 945-948.
58. Shan Meng and Youwei Zhang (2003) A method of visual speech feature area localization. Proceedings of the International Conference on Neural Networks and Signal Processing, 2003, vol. 2, 14-17 Dec., pp. 1173-1176.
59. Sun-Yuan Kung and Jenq-Neng Hwang (1998) Neural networks for intelligent multimedia processing. Proceedings of the IEEE Workshop on Neural Networksm, vol. 86, no. 6, pp. 1244-1272.
60. Frankel J., Richmond K., King S., and Taylor P. (2000) An automatic speech recognition system using neural networks and linear dynamic models to recover and model articulatory traces. Proc. ICSLP, 2000.
61. Schuller B., Reiter S., and Rigoll G. (2006) Evolutionary feature generation in speech emotion. IEEE International Conference on Recognition Multimedia, pp. 5-8.
62. Lewis T. W. and Powers D. M. W., Audio-visual speech recognition using red exclusion and neural networks. Proceedings of the twenty-fifth Australasian conference on Computer science, vol. 4, Melbourne, Victoria, Australia, pp. 149-156.
63. Nakamura S. (2002) Statistical multimodal integration for audio-visual speech processing. IEEE Transactions on Neural Networks, vol. 13, no. 4, pp. 854-866.
64. Guido R. C., Pereira J. C., and Slaets J. F. W. (2007) Advances on pattern recognition for speech and audio processing. Pattern Recognition Letters, vol. 28, no. 11, pp. 1283-1284.
65. Vahideh Sadat Sadeghi and Khashayar Yaghmaie (2006) Vowel recognition using neural networks. International Journal of Computer Science and Network Security (IJCSNS), vol. 6, no. 12, pp. 154-158.
66. Hartigan J. A. and Wong M. A. (1979) Algorithm AS136: A K-means clustering algorithm. Applied Statistics, vol. 28, pp. 100-108.
67. Henry C. and Peters J. F. (2007) Image pattern recognition using approximation spaces and near sets. In: Proceedings of Eleventh International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007), Joint Rough Set Symposium (JRS 2007), Lecture Notes in Artificial Intelligence, vol. 4482, pp. 475-482.
68. Kerre E. and Nachtegael M. (2000) Fuzzy techniques in image processing: Techniques and applications. Studies in Fuzziness and Soft Computing, vol. 52, Physica, Heidelberg.
69. Lingras P. and West C. (2004) Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems, vol. 23, no. 1, pp. 5-16.
70. Lingras P. (2007) Applications of rough set based K-means, Kohonen, GA Clustering. Transactions on Rough Sets, VII, pp. 120-139.
71. Mitra Sushmita (2004) An evolutionary rough partitive clustering. Pattern Recognition Letters, vol. 25, pp. 1439-1449.
72. Ng H. P., Ong S. H., Foong K. W. C., Goh P. S., and Nowinski, W. L. (2006) Medical image segmentation using K-means clustering and improved watershed algorithm. IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 61-65.
73. Nachtegael M., Van-Der-Weken M., Van-De-Ville D., Kerre D., Philips W., and Lemahieu I. (2001) An overview of classical and fuzzy-classical filters for noise reduction. 10th International IEEE Conference on Fuzzy Systems FUZZ-IEEE 2001, Melbourne, Australia, pp. 3-6.
74. Ning S., Ziarko W., Hamilton J., and Cercone N. (1995) Using rough sets as tools for knowledge discovery. In: Fayyad U. M. and Uthurusamy R. (eds.), First International Conference on Knowledge Discovery and Data Mining KDD'95, Montreal, Canada, AAAI, pp. 263-268.
75. Pawlak Z. (1991) Rough sets - Theoretical aspects of reasoning about data. Kluwer, Dordrecht.
76. Pawlak Z., Grzymala-Busse J., Slowinski R., and Ziarko W. (1995) Rough sets. Communications of the ACM, vol. 38, no. 11, pp. 88-95.
77. Polkowski L. (2003) Rough Sets: Mathematical Foundations. Physica, Heidelberg.
78. Peters J. F. (2007) Near sets: Special theory about nearness of objects. Fundamenta Informaticae, vol. 75, no. 1-4, pp. 407-433.
79. Peters J. F. (2007) Near sets. General theory about nearness of objects. Applied Mathematical Sciences, vol. 1, no. 53, pp. 2609-2029.
80. Peters J. F., Skowron A., and Stepaniuk J. (2007) Nearness of objects: Extension of approximation space model. Fundamenta Informaticae, vol. 79, pp. 1-16.
81. Peters J. F. (2007) Near sets. Toward approximation space-based object recognition, In: Yao Y., Lingras P., Wu W.-Z, Szczuka M., Cercone N., Slezak D. (eds.), Proc. of the Second Int. Conf. on Rough Sets and Knowledge Technology (RSKT07), Joint Rough Set Symposium (JRS07), Lecture Notes in Artificial Intelligence, vol. 4481, Springer, Berlin Heidelberg New York, pp. 22-33.
82. Peters J. F. and Ramanna S. (2007) Feature selection: Near set approach. In: Ras Z. W., Tsumoto S., and Zighed D. A. (eds.) 3rd Int. Workshop on Mining Complex Data (MCD'07), ECML/PKDD-2007, Lecture Notes in Artificial Intelligence, Springer, Berlin Heidelberg New York, in press.
83. Peters J. F., Skowron A., and Stepaniuk J. (2006) Nearness in approximation spaces. In: Lindemann G., Schlilngloff H. et al. (eds.), Proc. Concurrency, Specification & Programming (CS&P'2006), Informatik-Berichte Nr. 206, Humboldt-Universitat zu Berlin, pp. 434-445.
84. Orlowska E. (1982) Semantics of vague concepts. Applications of rough sets. Institute for Computer Science, Polish Academy of Sciences, Report 469, 1982.
85. See, also, Orlowska E., Semantics of vague concepts, In: Dorn G. and Weingartner P. (eds.), Foundations of Logic and Linguistics. Problems and Solutions, Plenum, London, 1985, pp. 465-482.
86. Orlowska E. (1990) Verisimilitude based on concept analysis. Studia Logica, vol. 49, no. 3, pp. 307-320.
87. Pawlak Z. (1981) Classification of objects by means of attributes. Institute for Computer Science, Polish Academy of Sciences, Report 429, 1981.
88. Pawlak Z. (1982) Rough sets. International Journal of Computing and Information Sciences, vol. 11, pp. 341-356.
89. Pawlak Z. and Skowron A. (2007) Rudiments of rough sets. Information Sciences, vol. 177, pp. 3-27.
90. Peters J. F. (2008) Classification of perceptual objects by means of features. International Journal of Information Technology and Intelligent Computing, vol. 3, no. 2, pp. 1-35.
91. Lockery D. and Peters J. F. (2007) Robotic target tracking with approximation space-based feedback during reinforcement learning. In: Proceedings of Eleventh International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007), Joint Rough Set Symposium (JRS 2007), Lecture Notes in Artificial Intelligence, vol. 4482, pp. 483-490.
92. Peters J. F., Borkowski M., Henry C, and Lockery D. (2006) Monocular vision system that learns with approximation spaces. In: Ella A., Lingras P., Slezak D., and Suraj Z. (eds.), Rough Set Computing: Toward Perception Based Computing, Idea Group Publishing, Hershey, PA, pp. 1-22.
93. Peters J. F., Borkowski M., Henry C, Lockery D., Gunderson D., and Ramanna S. (2006) Line-crawling bots that inspect electric power transmission line equipment. Proc. 3rd Int. Conf. on Autonomous Robots and Agents 2006 (ICARA 2006), Palmerston North, NZ, 2006, pp. 39-44.
94. Peters J. F. (2008) Approximation and perception in ethology-based reinforcement learning. In: Pedrycz W., Skowron A., and Kreinovich V. (eds.), Handbook on Granular Computing, Wiley, New York, Ch. 30, pp. 1-41.
95. Peters J. F. and Borkowski M. (2004) K-means indiscernibility relation over pixels. Proc. 4th Int. Conf. on Rough Sets and Current Trends in Computing (RSCTC 2004), Uppsala, Sweden, 1-5 June, pp. 580-585.
96. Peters J. F. and Pedrycz W. (2007) Computational intelligence. In: EEE Encyclopedia. Wiley, New York, in press.
97. Peters J. F., Liting H., and Ramanna S. (2001) Rough neural computing in signal analysis. Computational Intelligence, vol. 17, no. 3, pp. 493-513.
98. Peters J. F., Skowron A., Suraj Z., Rzasa W., Borkowski M. (2002) Clustering: A rough set approach to constructing information granules. Soft Computing and Distributed Processing. Proceedings of 6th International Conference, SCDP 2002, pp. 57-61.
99. Petrosino A. and Salvi G. (2006) Rough fuzzy set based scale space transforms and their use in image analysis. International Journal of Approximate Reasoning, vol. 41, no. 2, pp. 212-228.
100. Shankar B. U. (2007) Novel classification and segmentation techniques with application to remotely sensed images. Transactions on Rough Sets, vol. VII, LNCS 4400, pp. 295-380.
101. Otto C. W. (2007) Motivating rehabilitation exercise using instrumented objects to play video games via a configurable universal translation peripheral, M. Sc. Thesis, Supervisors: Peters J. F. and Szturm T., Department of Electrical and Computer Engineering, University of Manitoba, 2007.
102. Szturm T., Peters J. F., Otto C., Kapadia N., and Desai A. (2008) Taskspecific rehabilitation of finger-hand function using interactive computer gaming, Archives for Physical Medicine and Rehabilitation, submitted.
103. Sandeep Chandana and Rene V. Mayorga (2006) RANFIS: Rough adaptive neuro-fuzzy inference system. International Journal of Computational Intelligence, vol. 3, no. 4, pp. 289-295.
104. Swagatam Das, Ajith Abraham, and Subir Kumar Sarkar (2006) A hybrid rough set - Particle swarm algorithm for image pixel classification. Proceedings of the Sixth International Conference on Hybrid Intelligent Systems, 13-15 Dec., pp. 26-32.
105. Bezdek J. C., Ehrlich R., and Full W. (1984) FCM: The fuzzy C-means clustering algorithm. Computers and Geosciences, vol. 10, pp. 191-203.
106. Cetin O., Kantor A., King S., Bartels C., Magimai-Doss M., Frankel J., and Livescu K. (2007) An articulatory feature-based tandem approach and factored observation modeling. IEEE International Conference on Acoustics, Speech and Signal, ICASSP2007, Honolulu, HI, vol. 4, pp. IV-645-IV-648.
107. Raducanu B., Grana M., and Sussner P. (2001) Morphological neural networks for vision based self-localization. IEEE International Conference on Robotics and Automation, ICRA2001, vol. 2, pp. 2059-2064.
108. Ahmed M. N., Yamany S. M., Nevin M., and Farag A. A. (2003) A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193-199.
109. Yan M. X. H. and Karp J. S. (1994) Segmentation of 3D brain MR using an adaptive K-means clustering algorithm. IEEE Conference on Nuclear Science Symposium and Medical Imaging, vol. 4, pp. 1529-1533.
110. Voges K. E., Pope N. K. L. I., and Brown M. R. (2002) Cluster analysis of marketing data: A comparison of K-means, rough set, and rough genetic approaches. In: Abbas H. A., Sarker R. A., and Newton C. S. (eds.), Heuristics and Optimization for Knowledge Discovery, Idea Group Publishing, pp. 208-216.
111. Chen C. W., Luo J. B., and Parker K. J. (1998) Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. IEEE Transactions on Image Processing, vol. 7, no. 12, pp. 1673-1683.
112. Davis K. J. and Najarian K. (2001) Maximizing strength of digital watermarks using neural networks. International Joint Conference on Neural Networks, IJCNN 2001, vol. 4, pp. 2893-2898.
113. Sankar K. Pal (2001) Fuzzy image processing and recognition: Uncertainties handling and applications. International Journal of Image and Graphics, vol. 1, no. 2, pp. 169-195.
114. Yixin Chen and James Z. Wang (2002) A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1252-1267.
115. Yu Wanga Mingyue Dingb, Chengping Zhoub, and Ying Hub (2006) Interactive relevance feedback mechanism for image retrieval using rough set. Knowledge-Based Systems, vol. 19, no. 8, pp. 696-703.
116. Zadeh L. A. (1965) Fuzzy sets. Information and Control, vol. 8, pp. 338-353.
117. Zbigniew W. (1987) Rough approximation of shapes in pattern recognition. Computer Vision, Graphics, and Image Processing, vol. 40, no. 2, pp. 228-249.