Sorted evolutionary strategy based SOFM used for vector quantization

Ruirui Ji; Hong Zhu; Qieshi Zhang

Sorted evolutionary strategy based SOFM used for vector quantization

Ruirui Ji^*, Hong Zhu, Qieshi Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents a sorted evolutionary strategy based self-organizing feature map (SOFM) algorithm to improve the efficiency of vector quantization. The image samples are sorted according to the human vision sensitivity to ensure an optimal vision effect under the precondition of the globe minimum error. A similarity evaluation about code vector is introduced to the evolutionary algorithm to guarantee the variety of the code vector and the adaptability to the image. Experimental results show that the higher adaptability of codebook and better quality of reconstructed image.

Original language	English
Title of host publication	Proceedings of the 2004 International Conference on Information Acquisition, ICIA 2004
Editors	T. Mei, M. Meng, Y. Ge, T.J. Tarn, Z. Wang, H. Szu
Pages	331-334
Number of pages	4
Publication status	Published - 2004
Externally published	Yes
Event	2004 International Conference on Information Acquisition, ICIA 2004 - Hefei Duration: 2004 Jun 21 → 2004 Jun 25

Other

Other	2004 International Conference on Information Acquisition, ICIA 2004
City	Hefei
Period	04/6/21 → 04/6/25

Keywords

Fitness
Similarity
SOFM
Sorted evolutionary strategy
Vector quantization

ASJC Scopus subject areas

Engineering(all)

Cite this

@inproceedings{a8fa22354a6c4d56b0dd3be39e47025a,

title = "Sorted evolutionary strategy based SOFM used for vector quantization",

abstract = "This paper presents a sorted evolutionary strategy based self-organizing feature map (SOFM) algorithm to improve the efficiency of vector quantization. The image samples are sorted according to the human vision sensitivity to ensure an optimal vision effect under the precondition of the globe minimum error. A similarity evaluation about code vector is introduced to the evolutionary algorithm to guarantee the variety of the code vector and the adaptability to the image. Experimental results show that the higher adaptability of codebook and better quality of reconstructed image.",

keywords = "Fitness, Similarity, SOFM, Sorted evolutionary strategy, Vector quantization",

author = "Ruirui Ji and Hong Zhu and Qieshi Zhang",

year = "2004",

language = "English",

isbn = "0780386299",

pages = "331--334",

editor = "T. Mei and M. Meng and Y. Ge and T.J. Tarn and Z. Wang and H. Szu",

booktitle = "Proceedings of the 2004 International Conference on Information Acquisition, ICIA 2004",

note = "2004 International Conference on Information Acquisition, ICIA 2004 ; Conference date: 21-06-2004 Through 25-06-2004",

}

TY - GEN

T1 - Sorted evolutionary strategy based SOFM used for vector quantization

AU - Ji, Ruirui

AU - Zhu, Hong

AU - Zhang, Qieshi

PY - 2004

Y1 - 2004

N2 - This paper presents a sorted evolutionary strategy based self-organizing feature map (SOFM) algorithm to improve the efficiency of vector quantization. The image samples are sorted according to the human vision sensitivity to ensure an optimal vision effect under the precondition of the globe minimum error. A similarity evaluation about code vector is introduced to the evolutionary algorithm to guarantee the variety of the code vector and the adaptability to the image. Experimental results show that the higher adaptability of codebook and better quality of reconstructed image.

AB - This paper presents a sorted evolutionary strategy based self-organizing feature map (SOFM) algorithm to improve the efficiency of vector quantization. The image samples are sorted according to the human vision sensitivity to ensure an optimal vision effect under the precondition of the globe minimum error. A similarity evaluation about code vector is introduced to the evolutionary algorithm to guarantee the variety of the code vector and the adaptability to the image. Experimental results show that the higher adaptability of codebook and better quality of reconstructed image.

KW - Fitness

KW - Similarity

KW - SOFM

KW - Sorted evolutionary strategy

KW - Vector quantization

UR - http://www.scopus.com/inward/record.url?scp=21444445032&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=21444445032&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:21444445032

SN - 0780386299

SP - 331

EP - 334

BT - Proceedings of the 2004 International Conference on Information Acquisition, ICIA 2004

A2 - Mei, T.

A2 - Meng, M.

A2 - Ge, Y.

A2 - Tarn, T.J.

A2 - Wang, Z.

A2 - Szu, H.

T2 - 2004 International Conference on Information Acquisition, ICIA 2004

Y2 - 21 June 2004 through 25 June 2004

ER -

Sorted evolutionary strategy based SOFM used for vector quantization

Abstract

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Keywords

ASJC Scopus subject areas

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