Sorted evolutionary strategy based SOFM used for vector quantization

Ruirui Ji, Hong Zhu, Qieshi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings of the 2004 International Conference on Information Acquisition, ICIA 2004
EditorsT. Mei, M. Meng, Y. Ge, T.J. Tarn, Z. Wang, H. Szu
Pages331-334
Number of pages4
Publication statusPublished - 2004
Externally publishedYes
Event2004 International Conference on Information Acquisition, ICIA 2004 - Hefei
Duration: 2004 Jun 212004 Jun 25

Other

Other2004 International Conference on Information Acquisition, ICIA 2004
CityHefei
Period04/6/2104/6/25

Keywords

  • Fitness
  • Similarity
  • SOFM
  • Sorted evolutionary strategy
  • Vector quantization

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Ji, R., Zhu, H., & Zhang, Q. (2004). Sorted evolutionary strategy based SOFM used for vector quantization. In T. Mei, M. Meng, Y. Ge, T. J. Tarn, Z. Wang, & H. Szu (Eds.), Proceedings of the 2004 International Conference on Information Acquisition, ICIA 2004 (pp. 331-334)