An unsupervised fuzzy-neuro quantiser for image compression

An unsupervised fuzzy-neuro quantiser for image compression

We propose a competitive fuzzy-neuro model for image compression. This model is based on a new unsupervised fuzzy learning algorithm, designed for optimal training of competitive neural networks. Experimental results show that the proposed model can perform better than other well-known methods of it...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Madiafi, M., Bouroumi, A.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Boats
competitive neural networks
Image coding
image compression
Image reconstruction
Integrated circuits
unsupervised learning
vector quantization
Vectors
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 223
container_issue
container_start_page 218
container_title
container_volume
creator Madiafi, M.
Bouroumi, A.
description We propose a competitive fuzzy-neuro model for image compression. This model is based on a new unsupervised fuzzy learning algorithm, designed for optimal training of competitive neural networks. Experimental results show that the proposed model can perform better than other well-known methods of its category, including FCM and IFLVQ. Typical examples of these results are presented and discussed.
doi_str_mv 10.1109/ICMCS.2012.6320219
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6320219</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6320219</ieee_id><sourcerecordid>6320219</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-e00844961843f0e091794ab39fc2ef9686bacea25cd378d2f62285877b52cf6b3</originalsourceid><addsrcrecordid>eNo9T8tKw0AUHRFBrf0B3cwPJM69k8wDVyX4KFRctK7LZHJHIjaJM43Qfr0Bi2dzOGdxHozdgsgBhL1fVq_VOkcBmCuJAsGesWsolJZQolDn_wJMccnmKX2KCQZBWrxiD4uOj10aB4o_baKGh_F4PGQdjbHn36Pr9pMbeegjb3fug7jvd0OklNq-u2EXwX0lmp94xt6fHjfVS7Z6e15Wi1XWgi73GU1tRWHV1C-DIGFB28LV0gaPFKwyqnaeHJa-kdo0GBSiKY3WdYk-qFrO2N1fbktE2yFOQ-JhezorfwEBikiT</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An unsupervised fuzzy-neuro quantiser for image compression</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Madiafi, M. ; Bouroumi, A.</creator><creatorcontrib>Madiafi, M. ; Bouroumi, A.</creatorcontrib><description>We propose a competitive fuzzy-neuro model for image compression. This model is based on a new unsupervised fuzzy learning algorithm, designed for optimal training of competitive neural networks. Experimental results show that the proposed model can perform better than other well-known methods of its category, including FCM and IFLVQ. Typical examples of these results are presented and discussed.</description><identifier>ISBN: 1467315184</identifier><identifier>ISBN: 9781467315180</identifier><identifier>EISBN: 1467315206</identifier><identifier>EISBN: 1467315192</identifier><identifier>EISBN: 9781467315197</identifier><identifier>EISBN: 9781467315203</identifier><identifier>DOI: 10.1109/ICMCS.2012.6320219</identifier><language>eng</language><publisher>IEEE</publisher><subject>Boats ; competitive neural networks ; Image coding ; image compression ; Image reconstruction ; Integrated circuits ; unsupervised learning ; vector quantization ; Vectors</subject><ispartof>2012 International Conference on Multimedia Computing and Systems, 2012, p.218-223</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6320219$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6320219$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Madiafi, M.</creatorcontrib><creatorcontrib>Bouroumi, A.</creatorcontrib><title>An unsupervised fuzzy-neuro quantiser for image compression</title><title>2012 International Conference on Multimedia Computing and Systems</title><addtitle>ICMCS</addtitle><description>We propose a competitive fuzzy-neuro model for image compression. This model is based on a new unsupervised fuzzy learning algorithm, designed for optimal training of competitive neural networks. Experimental results show that the proposed model can perform better than other well-known methods of its category, including FCM and IFLVQ. Typical examples of these results are presented and discussed.</description><subject>Boats</subject><subject>competitive neural networks</subject><subject>Image coding</subject><subject>image compression</subject><subject>Image reconstruction</subject><subject>Integrated circuits</subject><subject>unsupervised learning</subject><subject>vector quantization</subject><subject>Vectors</subject><isbn>1467315184</isbn><isbn>9781467315180</isbn><isbn>1467315206</isbn><isbn>1467315192</isbn><isbn>9781467315197</isbn><isbn>9781467315203</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9T8tKw0AUHRFBrf0B3cwPJM69k8wDVyX4KFRctK7LZHJHIjaJM43Qfr0Bi2dzOGdxHozdgsgBhL1fVq_VOkcBmCuJAsGesWsolJZQolDn_wJMccnmKX2KCQZBWrxiD4uOj10aB4o_baKGh_F4PGQdjbHn36Pr9pMbeegjb3fug7jvd0OklNq-u2EXwX0lmp94xt6fHjfVS7Z6e15Wi1XWgi73GU1tRWHV1C-DIGFB28LV0gaPFKwyqnaeHJa-kdo0GBSiKY3WdYk-qFrO2N1fbktE2yFOQ-JhezorfwEBikiT</recordid><startdate>201205</startdate><enddate>201205</enddate><creator>Madiafi, M.</creator><creator>Bouroumi, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201205</creationdate><title>An unsupervised fuzzy-neuro quantiser for image compression</title><author>Madiafi, M. ; Bouroumi, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e00844961843f0e091794ab39fc2ef9686bacea25cd378d2f62285877b52cf6b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Boats</topic><topic>competitive neural networks</topic><topic>Image coding</topic><topic>image compression</topic><topic>Image reconstruction</topic><topic>Integrated circuits</topic><topic>unsupervised learning</topic><topic>vector quantization</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Madiafi, M.</creatorcontrib><creatorcontrib>Bouroumi, A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Madiafi, M.</au><au>Bouroumi, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An unsupervised fuzzy-neuro quantiser for image compression</atitle><btitle>2012 International Conference on Multimedia Computing and Systems</btitle><stitle>ICMCS</stitle><date>2012-05</date><risdate>2012</risdate><spage>218</spage><epage>223</epage><pages>218-223</pages><isbn>1467315184</isbn><isbn>9781467315180</isbn><eisbn>1467315206</eisbn><eisbn>1467315192</eisbn><eisbn>9781467315197</eisbn><eisbn>9781467315203</eisbn><abstract>We propose a competitive fuzzy-neuro model for image compression. This model is based on a new unsupervised fuzzy learning algorithm, designed for optimal training of competitive neural networks. Experimental results show that the proposed model can perform better than other well-known methods of its category, including FCM and IFLVQ. Typical examples of these results are presented and discussed.</abstract><pub>IEEE</pub><doi>10.1109/ICMCS.2012.6320219</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 1467315184
ispartof 2012 International Conference on Multimedia Computing and Systems, 2012, p.218-223
issn
language eng
recordid cdi_ieee_primary_6320219
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Boats
competitive neural networks
Image coding
image compression
Image reconstruction
Integrated circuits
unsupervised learning
vector quantization
Vectors
title An unsupervised fuzzy-neuro quantiser for image compression
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T11%3A29%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20unsupervised%20fuzzy-neuro%20quantiser%20for%20image%20compression&rft.btitle=2012%20International%20Conference%20on%20Multimedia%20Computing%20and%20Systems&rft.au=Madiafi,%20M.&rft.date=2012-05&rft.spage=218&rft.epage=223&rft.pages=218-223&rft.isbn=1467315184&rft.isbn_list=9781467315180&rft_id=info:doi/10.1109/ICMCS.2012.6320219&rft_dat=%3Cieee_6IE%3E6320219%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1467315206&rft.eisbn_list=1467315192&rft.eisbn_list=9781467315197&rft.eisbn_list=9781467315203&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6320219&rfr_iscdi=true