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Output details

11 - Computer Science and Informatics

University of Westminster

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Output 43 of 79 in the submission

Article title

Improved batch fuzzy learning vector quantization for image compression

Type

D - Journal article

DOI

10.1016/j.ins.2008.05.017

Title of journal

Information Sciences

Article number

Volume number

178

Issue number

First page of article

3895

ISSN of journal

0020-0255

Year of publication

2008

URL

http://westminsterresearch.wmin.ac.uk/7357/

Number of additional authors

Additional information

<23>Originality: The paper presents the development and evaluation of a novel batch fuzzy learning vector quantisation (FLVQ) algorithm. It addresses and solves the following problems: (a) selection of the initial value for the learning rate coinciding with the fuzziness parameter, (b) methodology for reducing high computational cost, (c) the transfer of training vectors to crisp mode.

Significance: The problem solved in this paper plays an important role in various image compression approaches.

Rigour: All major findings of this research are supported by mathematical analyses. Results published in a leading peer-reviewed journal.

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract