The enhanced LBG algorithm

Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups...

Full description

Saved in:
Bibliographic Details
Published in:Neural networks 2001-11, Vol.14 (9), p.1219-1237
Main Authors: Patané, Giuseppe, Russo, Marco
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Clustering
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Fuzzy c-means
GLA
Hard c-means
Image Processing, Computer-Assisted - methods
K-means
LBG
LVQ
Neural Networks (Computer)
Pattern recognition. Digital image processing. Computational geometry
Stochastic Processes
Unsupervised learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3
cites cdi_FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3
container_end_page 1237
container_issue 9
container_start_page 1219
container_title Neural networks
container_volume 14
creator Patané, Giuseppe
Russo, Marco
description Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.
doi_str_mv 10.1016/S0893-6080(01)00104-6
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_72298587</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0893608001001046</els_id><sourcerecordid>26996546</sourcerecordid><originalsourceid>FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3</originalsourceid><addsrcrecordid>eNqFkE1PAjEURRujEUT_gImGjUYXo--1pdOujBJFExIX4rrp9EPGDIy2YOK_dwaILFm9zbn3vhxCzhBuEFDcvoFULBMg4QrwGgCBZ2KPdFHmKqO5pPuk-490yFFKnwAgJGeHpIOYo-SUdsnpZOr7fj41c-tdf_ww6pvqo47lYjo7JgfBVMmfbG6PvD89TobP2fh19DK8H2eWD3CRWckoQ5Mr7gIFjqEIHsBLK5RhBTjqGOZBFYWxGFCp4KxUnAk74MLJ4FiPXK57v2L9vfRpoWdlsr6qzNzXy6RzSpUcyHwnSIVSomltwMEatLFOKfqgv2I5M_FXI-jWnl7Z060aDahX9nSbO98MLIuZd9vURlcDXGwAk6ypQmy0lWnLcVCSsrbobs35xttP6aNOtvSt4jJ6u9CuLne88gd6_IkY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>26996546</pqid></control><display><type>article</type><title>The enhanced LBG algorithm</title><source>ScienceDirect Freedom Collection 2022-2024</source><creator>Patané, Giuseppe ; Russo, Marco</creator><creatorcontrib>Patané, Giuseppe ; Russo, Marco</creatorcontrib><description>Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.</description><identifier>ISSN: 0893-6080</identifier><identifier>EISSN: 1879-2782</identifier><identifier>DOI: 10.1016/S0893-6080(01)00104-6</identifier><identifier>PMID: 11718422</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algorithms ; Applied sciences ; Artificial intelligence ; Clustering ; Computer science; control theory; systems ; Connectionism. Neural networks ; Exact sciences and technology ; Fuzzy c-means ; GLA ; Hard c-means ; Image Processing, Computer-Assisted - methods ; K-means ; LBG ; LVQ ; Neural Networks (Computer) ; Pattern recognition. Digital image processing. Computational geometry ; Stochastic Processes ; Unsupervised learning</subject><ispartof>Neural networks, 2001-11, Vol.14 (9), p.1219-1237</ispartof><rights>2001 Elsevier Science Ltd</rights><rights>2002 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3</citedby><cites>FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=14098236$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11718422$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Patané, Giuseppe</creatorcontrib><creatorcontrib>Russo, Marco</creatorcontrib><title>The enhanced LBG algorithm</title><title>Neural networks</title><addtitle>Neural Netw</addtitle><description>Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Clustering</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Fuzzy c-means</subject><subject>GLA</subject><subject>Hard c-means</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>K-means</subject><subject>LBG</subject><subject>LVQ</subject><subject>Neural Networks (Computer)</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Stochastic Processes</subject><subject>Unsupervised learning</subject><issn>0893-6080</issn><issn>1879-2782</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqFkE1PAjEURRujEUT_gImGjUYXo--1pdOujBJFExIX4rrp9EPGDIy2YOK_dwaILFm9zbn3vhxCzhBuEFDcvoFULBMg4QrwGgCBZ2KPdFHmKqO5pPuk-490yFFKnwAgJGeHpIOYo-SUdsnpZOr7fj41c-tdf_ww6pvqo47lYjo7JgfBVMmfbG6PvD89TobP2fh19DK8H2eWD3CRWckoQ5Mr7gIFjqEIHsBLK5RhBTjqGOZBFYWxGFCp4KxUnAk74MLJ4FiPXK57v2L9vfRpoWdlsr6qzNzXy6RzSpUcyHwnSIVSomltwMEatLFOKfqgv2I5M_FXI-jWnl7Z060aDahX9nSbO98MLIuZd9vURlcDXGwAk6ypQmy0lWnLcVCSsrbobs35xttP6aNOtvSt4jJ6u9CuLne88gd6_IkY</recordid><startdate>20011101</startdate><enddate>20011101</enddate><creator>Patané, Giuseppe</creator><creator>Russo, Marco</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>20011101</creationdate><title>The enhanced LBG algorithm</title><author>Patané, Giuseppe ; Russo, Marco</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Clustering</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Fuzzy c-means</topic><topic>GLA</topic><topic>Hard c-means</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>K-means</topic><topic>LBG</topic><topic>LVQ</topic><topic>Neural Networks (Computer)</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Stochastic Processes</topic><topic>Unsupervised learning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Patané, Giuseppe</creatorcontrib><creatorcontrib>Russo, Marco</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><jtitle>Neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Patané, Giuseppe</au><au>Russo, Marco</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The enhanced LBG algorithm</atitle><jtitle>Neural networks</jtitle><addtitle>Neural Netw</addtitle><date>2001-11-01</date><risdate>2001</risdate><volume>14</volume><issue>9</issue><spage>1219</spage><epage>1237</epage><pages>1219-1237</pages><issn>0893-6080</issn><eissn>1879-2782</eissn><abstract>Clustering applications cover several fields such as audio and video data compression, pattern recognition, computer vision, medical image recognition, etc. In this paper, we present a new clustering algorithm called Enhanced LBG (ELBG). It belongs to the hard and K-means vector quantization groups and derives directly from the simpler LBG. The basic idea we have developed is the concept of utility of a codeword, a powerful instrument to overcome one of the main drawbacks of clustering algorithms: generally, the results achieved are not good in the case of a bad choice of the initial codebook. We will present our experimental results showing the ELBG is able to find better codebooks than previous clustering techniques and the computational complexity is virtually the same as the simpler LBG.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><pmid>11718422</pmid><doi>10.1016/S0893-6080(01)00104-6</doi><tpages>19</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0893-6080
ispartof Neural networks, 2001-11, Vol.14 (9), p.1219-1237
issn 0893-6080
1879-2782
language eng
recordid cdi_proquest_miscellaneous_72298587
source ScienceDirect Freedom Collection 2022-2024
subjects Algorithms
Applied sciences
Artificial intelligence
Clustering
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Fuzzy c-means
GLA
Hard c-means
Image Processing, Computer-Assisted - methods
K-means
LBG
LVQ
Neural Networks (Computer)
Pattern recognition. Digital image processing. Computational geometry
Stochastic Processes
Unsupervised learning
title The enhanced LBG algorithm
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T19%3A43%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20enhanced%20LBG%20algorithm&rft.jtitle=Neural%20networks&rft.au=Patan%C3%A9,%20Giuseppe&rft.date=2001-11-01&rft.volume=14&rft.issue=9&rft.spage=1219&rft.epage=1237&rft.pages=1219-1237&rft.issn=0893-6080&rft.eissn=1879-2782&rft_id=info:doi/10.1016/S0893-6080(01)00104-6&rft_dat=%3Cproquest_cross%3E26996546%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c451t-c83231a794df2041fbfe00e8c69a3b0d2d317f9bbac1f199fdc89436c546d8fd3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=26996546&rft_id=info:pmid/11718422&rfr_iscdi=true