研究資料首頁-> 期刊論文
研究資料明細
| 論文名稱 | Variable-Branch Tree-Structured Vector Quantization |
| 發表日期 | 2004-12-01 |
| 論文收錄分類 | SCI |
| 所有作者 | Shiueng Bien Yang |
| 作者順序 | 第一作者 |
| 通訊作者 | 否 |
| 刊物名稱 | International Journal of Image and Graphics |
| 發表卷數 | 8 |
| 是否具有審稿制度 | 是 |
| 發表期數 | 1 |
| 期刊或學報出版地國別/地區 | NATSGP-新加坡共和國 |
| 發表年份 | 2008 |
| 發表月份 | 12 |
| 發表形式 | 電子期刊 |
| 所屬計劃案 | 無 |
| 可公開文檔 |
|
| 可公開文檔 |
|
| 可公開文檔 | |
| 附件 |
 Variable-Branch Tree-Structured Vector Quantization.pdf
|
[英文摘要] :
Tree-structured vector quantizers (TSVQ) and their
variants have recently been proposed. All trees used are fixed
M-ary tree structured, such that the training samples in each
node must be artificially divided into a fixed number of clusters.
This paper proposes a variable-branch tree-structured vector
quantizer (VBTSVQ) based on a genetic algorithm, which searches
for the number of child nodes of each splitting node for optimal
coding in VBTSVQ. Moreover, one disadvantage of TSVQ is that
the searched codeword usually differs from the full searched
codeword. Briefly, the searched codeword in TSVQ sometimes is
not the closest codeword to the input vector. This paper proposes
the multiclassification encoding method to select many classified
components to represent each cluster, and the codeword encoded in
theVBTSVQis usually the same as that of the full search.VBTSVQ
outperforms other TSVQs in the experiments presented here.
[參考文獻] :
[1] A. Buzo, A. H. Gray Jr., R. M. Gray, and J. Markel, “Speech coding
based upon vector quantization,” IEEE Trans. Acoust., Speech, Signal
Processing, vol. ASSP-28, pp. 562–574, May 1985.
[2] J. Makhoul, S. Roucos, and H. Gish, “Vector quantization in speech
coding,” Proc. IEEE, vol. 73, pp. 1551–1588, Nov. 1985.
[3] P. A. Chou, T. Lookabaugh, and R. M. Gray, “Optimal pruning with applications
to tree-structured source coding and modeling,” IEEE Trans.
Inform. Theory, vol. 35, pp. 299–315, Mar. 1989.
[4] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification
and Regression Trees, ser. The Wadsworth Statistics/Probability
Series. Belmont, CA: Wadsworth, 1984.
[5] E. A. Riskin and R. M. Gray, “A greedy tree growing algorithm for
the design of variable rate vector quantizers,” IEEE Trans. Signal Processing,
vol. 39, pp. 2500–2507, Nov. 1991.
[6] M. Balakrishnan, W. A. Pearlman, and L. Lu, “Variable-rate tree-structured
vector quantizers,” IEEE Trans. Inform. Theory, vol. 41, pp.
917–930, July 1995.
[7] U. Bayazit and W. A. Pearlman, “Variable-length constrained-storage
tree-structured vector quantization,” IEEE Trans. Image Processing, vol.
8, pp. 321–331, Mar. 1999.
[8] Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantizer
design,” IEEE Trans. Commun., vol. 28, pp. 84–95, Jan. 1980.
[9] S. Z. Selim and M. A. Ismail, “K-means-type algorithm: Generalized
convergence theorem and characterization of local optimality,” IEEE
Trans. Pattern Anal. Machine Intell., vol. 6, pp. 81–87, Jan. 1984.
[10] M. Srinivas and M. Patnaik, “Genetic algorithm—Asurvey,” IEEE Computer,
vol. 27, pp. 17–26, June 1994.
[11] D. E. Goldberg, Genetic Algorithm in Search, Optimization, and Machine
Learning. Reading, MA: Addison-Wesley, 1989.
[12] C. C. Chang and T. S. Chen, “New tree-structured vector quantization
with closed-coupled multipath searching method,” Opt. Eng., vol. 36,
no. 6, pp. 1713–1720, 1997.
[13] S. L. Chiu, “Fuzzy model identification based on cluster estimation,” J.
Intell. Fuzzy Syst., vol. 2, no. 23, pp. 267–278, 1994.
[14] C. W. Tao, “Unsupervised fuzzy clustering with multicenter clusters,”
Fuzzy Sets Syst., vol. 128, pp. 305–322, 2002.
[15] T. H. Cormen, C. E. Leiserson, and R. L. Rivest, Introduction to Algorithms.
Cambridge, MA: MIT Press, 1990.
[16] W. H. Equitz, “A new vector quantization clustering algorithm,” IEEE
Trans. Acoust. Speech Signal Processing, vol. ASSP-32, pp. 1568–1575,
Sept. 1989.
