Effect of Fuzzy and Crisp Clustering Algorithms to Design Code Book for Vector Quantization in Applications
Yukinori Suzuki, Hiromu Sakakita, Junji Maeda
2015
Abstract
Image coding technologies are widely studies not only to economize storage device but also to use communication channel effectively. In various image coding technologies, we have been studied vector quantization. Vector quantization technology does not cause deterioration image quality in a high compression region and also has a negligible computational cost for image decoding. It is therefore useful technology for communication terminals with small payloads and small computational costs. Furthermore, it is also useful for biomedical signal processing: medical imaging and medical ultrasound image compression. Encoded and/or decoded image quality depends on a code book that is constructed in advance. In vector quantization, a code book determines the performance. Various clustering algorithms were proposed to design a code book. In this paper, we examined effect of typical clustering (crisp clustering and fuzzy clustering) algorithms in terms of applications of vector quantization. Two sets of experiments were carried out for examination. In the first set of experiments, the learning image to construct a code book was the same as the test image. In practical vector quantization, learning images are different from test images. Therefore, learning images that were different from test images were used in the second set of experiments. The first set of experiments showed that selection of a clustering algorithm is important for vector quantization. However, the second set of experiments showed that there is no notable difference in performance of the clustering algorithms. For practical applications of vector quantization, the choice of clustering algorithms to design a code book is not important.
References
- H. A. Abbass, MBO: Marriage in honey bees optimization a haplometrosis polygynous swarming approach, Proceedings of the 2001 Congress on Evolutionary Computation, 1, pp. 207-214, 2001.
- C. Amerijckx, M. Verleysen, P. Thissen, J-D. Legat, Image compression by self-organizing Kohonen map, IEEE Trans. Neural Networks, 9, pp.503-507, 1998.
- J. C Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press:New York, 1997.
- H. Feng, C. Chen, F. Ye, Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression, Expert Systems Applications, 32, pp. 213-222, 2007.
- M. Fujibayashi, T. Nozawa, T. Nakayama, K. Mochizuki, M. Konda, K. Kotani, S. Sugawara, T. Ohmi, A stillimage encoder based on adaptive resolution vector quantization featuring needless calculation eliminaS. M. Hosseini, A-R. Naghsh-Nilchi, Medical ultrasound image compression using contextual vector quantization, Computer in Biology and medicine, 42, pp. 743- 750, 2012.
- W-L Hung, D-H. Chen, M-S. Yang, Suppressed fuzzysoft learning vector quantization for MRI segmentation, Artificial Intelligence in Medicine, 52, pp. 33-43, 2011.
- A. K. Jain, M. N. Murty, P. J. Flynn, Data clustering: a review, ACM Computing Surveys, 31, 3, pp. 264-323, 1999.
- N. B. Karayiannis, P. Pai, Fuzzy vector quantization algorithms and their application in image compression, IEEE Trans. Image Processing, 4, 9, pp. 1193-1201, 1995.
- A. Laha, N. R. Pal, B. Chanda, Design of vector quantizer for image compression using self-organizing feature map and surface fitting, IEEE Trans. Image Processing, 13, 10, pp. 1291-1303, 2004.
- S. Lazebnik, M. Raginsky, Supervised learning of quantizer codebooks by information loss minimization, IEEE Trans on PAMI, 31, 7, pp. 1294-1309, 2009.
- Y. Linde, A. Buso, R. Gray, 1980. An algorithm for vector quantization design, IEEE Trans Communications, 28, 1, pp. 84-94, 1980.
- H. Matsumoto, F. Kichikawa, K. Sasazaki, J. Maeda, Y. Suzuki, Image compression using vector quantization with variable block size division, IEEJ Trans EIS, 130, 8, pp. 1431-1439, 2010.
- T. Miyamoto, Y. Suzuki, S. Saga, J. Maeda, Vector quantization of images using fractal dimensions, 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Application, pp. 214-217, 2005.
- B. P. Nguyen, C-K. Chui, S-H. Ong, S. Chang, An efficient compression scheme for 4-D medical imaging using hierarchical vector quantization and motion compensation, Computer and Biology and Medicine, 41, pp. 843-856, 2011.
- G. Patane, M. Russo, The enhanced LBG algorithm, Neural Networks, 14, pp. 1219-1237, 2001.
- K. Sasazaki, S. Saga, J. Maeda, Y. Suzuki, Vector quantization of images with variable block size, Applied Soft Computing, 8, pp. 634-645, 2008.
- K. Sayood, Introduction to data compression, Morgan Kaufmann Publisher:Boston, 2000.
- E. C-K. Tsao, J.C. Bezdek, N.R. Pal., Fuzzy Kohonen clustering networks, Pattern Recognition, 27, 5, pp. 757- 764, 1994.
- G. E. Tsekouras, A fuzzy vector quantization approach to image compression, Applied Mathematics and Computation, 167, pp. 539-560, 2005.
- G. E. Tsekouras, M. Antonios, C. Anagnostopoulos, D. Gavalas, D. Economou, Improved batch fuzzy learning vector quantization for image compression, Information Sciences, 178, pp. 3895-3907, 2008.
- D. Tsolakis, G.E. Tsekouras, J. Tsimikas, Fuzzy vector quantization for image compression based on competitive agglomeration and a novel codeword migration strategy, Engineering applications of artificial intelligence, 25, pp. 1212-1225, 2012.
- Y. Wang, X. Y. Feng, X, Y. X. Huang, D. B. Pu, W. G. Zhou, Y. C. Liang, C-G. Zhou, A novel quantum swarm evolutionary algorithm and its applications, Neurocomputing, 70, pp. 633-640, 2007.
Paper Citation
in Harvard Style
Suzuki Y., Sakakita H. and Maeda J. (2015). Effect of Fuzzy and Crisp Clustering Algorithms to Design Code Book for Vector Quantization in Applications . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 198-205. DOI: 10.5220/0005207501980205
in Bibtex Style
@conference{biosignals15,
author={Yukinori Suzuki and Hiromu Sakakita and Junji Maeda},
title={Effect of Fuzzy and Crisp Clustering Algorithms to Design Code Book for Vector Quantization in Applications},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={198-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005207501980205},
isbn={978-989-758-069-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Effect of Fuzzy and Crisp Clustering Algorithms to Design Code Book for Vector Quantization in Applications
SN - 978-989-758-069-7
AU - Suzuki Y.
AU - Sakakita H.
AU - Maeda J.
PY - 2015
SP - 198
EP - 205
DO - 10.5220/0005207501980205