A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach

In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and...

Full description

Saved in:
Bibliographic Details
Main Authors: Tlig, Lotfi, Sayadi, Mounir, Fnaeich, Farhat
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Clustering algorithms
Feature extraction
Frequency modulation
Fuzzy clustering
Gabor filtering
Image segmentation
Local binary pattern
Partitioning algorithms
Pixel
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 263
container_issue
container_start_page 258
container_title
container_volume
creator Tlig, Lotfi
Sayadi, Mounir
Fnaeich, Farhat
description In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers the optimized filters. Second, it aims to characterize both micro and macro textures. In addition, an extended version of a type 2 fuzzy c-means clustering algorithm is proposed. The extension is based on the integration of spatial information in the membership function (MF). The performance of this method is demonstrated by several experiments on natural textures.
doi_str_mv 10.1109/IPTA.2010.5586746
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_5586746</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5586746</ieee_id><sourcerecordid>5586746</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-123381ad7e38a834609e0bd49713ebd62074e5e997ed4eb34242ae8a576d17633</originalsourceid><addsrcrecordid>eNo9kN1KAzEQheMfWOs-gHiTF9ia_2wuS7FaKOhFL8Sbkm1m10i7uyQp2j69AasHhjnDB8PMQeiOkgmlxDwsXlfTCSN5lLJSWqgzVBhdUcGE0EwYc45GjEpRSsreLtDNH9Di8h9Qeo2KGD9JlpBMSTJC71PcwRd2EDfBD6kPuMmV4DvtAzjsd7YFHKHdQZds8n2HaxszyKbZH48HnA4DlAxvtvuYIPiuxXYYQm83H7foqrHbCMWpj9Fq_riaPZfLl6fFbLosvSGppIzzilqngVe24kIRA6R2wmjKoXaKES1AgjEanICa57-YhcpKrRzVivMxuv9d6wFgPYR8cjisTynxH5R5Vzo</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach</title><source>IEEE Xplore All Conference Series</source><creator>Tlig, Lotfi ; Sayadi, Mounir ; Fnaeich, Farhat</creator><creatorcontrib>Tlig, Lotfi ; Sayadi, Mounir ; Fnaeich, Farhat</creatorcontrib><description>In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers the optimized filters. Second, it aims to characterize both micro and macro textures. In addition, an extended version of a type 2 fuzzy c-means clustering algorithm is proposed. The extension is based on the integration of spatial information in the membership function (MF). The performance of this method is demonstrated by several experiments on natural textures.</description><identifier>ISSN: 2154-5111</identifier><identifier>ISBN: 1424472474</identifier><identifier>ISBN: 9781424472475</identifier><identifier>EISSN: 2154-512X</identifier><identifier>EISBN: 9781424472499</identifier><identifier>EISBN: 9781424472482</identifier><identifier>EISBN: 1424472482</identifier><identifier>EISBN: 1424472490</identifier><identifier>DOI: 10.1109/IPTA.2010.5586746</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Clustering algorithms ; Feature extraction ; Frequency modulation ; Fuzzy clustering ; Gabor filtering ; Image segmentation ; Local binary pattern ; Partitioning algorithms ; Pixel</subject><ispartof>2010 2nd International Conference on Image Processing Theory, Tools and Applications, 2010, p.258-263</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/5586746$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5586746$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tlig, Lotfi</creatorcontrib><creatorcontrib>Sayadi, Mounir</creatorcontrib><creatorcontrib>Fnaeich, Farhat</creatorcontrib><title>A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach</title><title>2010 2nd International Conference on Image Processing Theory, Tools and Applications</title><addtitle>IPTA</addtitle><description>In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers the optimized filters. Second, it aims to characterize both micro and macro textures. In addition, an extended version of a type 2 fuzzy c-means clustering algorithm is proposed. The extension is based on the integration of spatial information in the membership function (MF). The performance of this method is demonstrated by several experiments on natural textures.</description><subject>Accuracy</subject><subject>Clustering algorithms</subject><subject>Feature extraction</subject><subject>Frequency modulation</subject><subject>Fuzzy clustering</subject><subject>Gabor filtering</subject><subject>Image segmentation</subject><subject>Local binary pattern</subject><subject>Partitioning algorithms</subject><subject>Pixel</subject><issn>2154-5111</issn><issn>2154-512X</issn><isbn>1424472474</isbn><isbn>9781424472475</isbn><isbn>9781424472499</isbn><isbn>9781424472482</isbn><isbn>1424472482</isbn><isbn>1424472490</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kN1KAzEQheMfWOs-gHiTF9ia_2wuS7FaKOhFL8Sbkm1m10i7uyQp2j69AasHhjnDB8PMQeiOkgmlxDwsXlfTCSN5lLJSWqgzVBhdUcGE0EwYc45GjEpRSsreLtDNH9Di8h9Qeo2KGD9JlpBMSTJC71PcwRd2EDfBD6kPuMmV4DvtAzjsd7YFHKHdQZds8n2HaxszyKbZH48HnA4DlAxvtvuYIPiuxXYYQm83H7foqrHbCMWpj9Fq_riaPZfLl6fFbLosvSGppIzzilqngVe24kIRA6R2wmjKoXaKES1AgjEanICa57-YhcpKrRzVivMxuv9d6wFgPYR8cjisTynxH5R5Vzo</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Tlig, Lotfi</creator><creator>Sayadi, Mounir</creator><creator>Fnaeich, Farhat</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach</title><author>Tlig, Lotfi ; Sayadi, Mounir ; Fnaeich, Farhat</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-123381ad7e38a834609e0bd49713ebd62074e5e997ed4eb34242ae8a576d17633</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accuracy</topic><topic>Clustering algorithms</topic><topic>Feature extraction</topic><topic>Frequency modulation</topic><topic>Fuzzy clustering</topic><topic>Gabor filtering</topic><topic>Image segmentation</topic><topic>Local binary pattern</topic><topic>Partitioning algorithms</topic><topic>Pixel</topic><toplevel>online_resources</toplevel><creatorcontrib>Tlig, Lotfi</creatorcontrib><creatorcontrib>Sayadi, Mounir</creatorcontrib><creatorcontrib>Fnaeich, Farhat</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 Xplore</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>Tlig, Lotfi</au><au>Sayadi, Mounir</au><au>Fnaeich, Farhat</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach</atitle><btitle>2010 2nd International Conference on Image Processing Theory, Tools and Applications</btitle><stitle>IPTA</stitle><date>2010-07</date><risdate>2010</risdate><spage>258</spage><epage>263</epage><pages>258-263</pages><issn>2154-5111</issn><eissn>2154-512X</eissn><isbn>1424472474</isbn><isbn>9781424472475</isbn><eisbn>9781424472499</eisbn><eisbn>9781424472482</eisbn><eisbn>1424472482</eisbn><eisbn>1424472490</eisbn><abstract>In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers the optimized filters. Second, it aims to characterize both micro and macro textures. In addition, an extended version of a type 2 fuzzy c-means clustering algorithm is proposed. The extension is based on the integration of spatial information in the membership function (MF). The performance of this method is demonstrated by several experiments on natural textures.</abstract><pub>IEEE</pub><doi>10.1109/IPTA.2010.5586746</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2154-5111
ispartof 2010 2nd International Conference on Image Processing Theory, Tools and Applications, 2010, p.258-263
issn 2154-5111
2154-512X
language eng
recordid cdi_ieee_primary_5586746
source IEEE Xplore All Conference Series
subjects Accuracy
Clustering algorithms
Feature extraction
Frequency modulation
Fuzzy clustering
Gabor filtering
Image segmentation
Local binary pattern
Partitioning algorithms
Pixel
title A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T18%3A40%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20new%20descriptor%20for%20textured%20image%20segmentation%20based%20on%20fuzzy%20type-2%20clustering%20approach&rft.btitle=2010%202nd%20International%20Conference%20on%20Image%20Processing%20Theory,%20Tools%20and%20Applications&rft.au=Tlig,%20Lotfi&rft.date=2010-07&rft.spage=258&rft.epage=263&rft.pages=258-263&rft.issn=2154-5111&rft.eissn=2154-512X&rft.isbn=1424472474&rft.isbn_list=9781424472475&rft_id=info:doi/10.1109/IPTA.2010.5586746&rft.eisbn=9781424472499&rft.eisbn_list=9781424472482&rft.eisbn_list=1424472482&rft.eisbn_list=1424472490&rft_dat=%3Cieee_CHZPO%3E5586746%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-123381ad7e38a834609e0bd49713ebd62074e5e997ed4eb34242ae8a576d17633%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5586746&rfr_iscdi=true