Image Segmentation as Learning on Hypergraphs
In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as the objects and background. Using the seed pixels, our method predicts the labels for all unlabeled pixels. We present the...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 252 |
|---|---|
| container_issue | |
| container_start_page | 247 |
| container_title | |
| container_volume | |
| creator | Lei Ding Yilmaz, A. |
| description | In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as the objects and background. Using the seed pixels, our method predicts the labels for all unlabeled pixels. We present the relations of the proposed method to other hypergraph based learning techniques. We give an adaptive procedure for constructing image hypergraphs and achieve promising results on a real image dataset. |
| doi_str_mv | 10.1109/ICMLA.2008.17 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4724982</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4724982</ieee_id><sourcerecordid>4724982</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-112e278d7af98226bd35048d4866fd93dff286abbf5a7234231654acb2c15a573</originalsourceid><addsrcrecordid>eNotjL1OwzAURi2hSqWlIxNLXiDB1_8eqwhopFQM0Lm6qe1gREJkZ-nbEwTD0afzDYeQe6AVALWPTX1s9xWj1FSgb8iGamUlFwsrsvm9LTUS-Jrscv6klIJVGqS5JWUzYO-LN98Pfpxxjt9jgbloPaYxjn2x6OE6-dQnnD7yHVkF_Mp-979bcnp-eq8PZfv60tT7toyg5VwCMM-0cRqDNYypznFJhXHCKBWc5S4EZhR2XZCoGReMg5ICLx27gESp-ZY8_HWj9_48pThgup6FZmLp8R_kvkFc</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Image Segmentation as Learning on Hypergraphs</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Lei Ding ; Yilmaz, A.</creator><creatorcontrib>Lei Ding ; Yilmaz, A.</creatorcontrib><description>In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as the objects and background. Using the seed pixels, our method predicts the labels for all unlabeled pixels. We present the relations of the proposed method to other hypergraph based learning techniques. We give an adaptive procedure for constructing image hypergraphs and achieve promising results on a real image dataset.</description><identifier>ISBN: 0769534953</identifier><identifier>ISBN: 9780769534954</identifier><identifier>DOI: 10.1109/ICMLA.2008.17</identifier><identifier>LCCN: 2008908513</identifier><language>eng</language><publisher>IEEE</publisher><subject>Application software ; Computer vision ; Humans ; hypergraphs ; Image segmentation ; Iterative algorithms ; Laplace equations ; Laplacian matrix ; Learning systems ; Machine learning ; Pixel ; State estimation</subject><ispartof>2008 Seventh International Conference on Machine Learning and Applications, 2008, p.247-252</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/4724982$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4724982$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lei Ding</creatorcontrib><creatorcontrib>Yilmaz, A.</creatorcontrib><title>Image Segmentation as Learning on Hypergraphs</title><title>2008 Seventh International Conference on Machine Learning and Applications</title><addtitle>ICMLA</addtitle><description>In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as the objects and background. Using the seed pixels, our method predicts the labels for all unlabeled pixels. We present the relations of the proposed method to other hypergraph based learning techniques. We give an adaptive procedure for constructing image hypergraphs and achieve promising results on a real image dataset.</description><subject>Application software</subject><subject>Computer vision</subject><subject>Humans</subject><subject>hypergraphs</subject><subject>Image segmentation</subject><subject>Iterative algorithms</subject><subject>Laplace equations</subject><subject>Laplacian matrix</subject><subject>Learning systems</subject><subject>Machine learning</subject><subject>Pixel</subject><subject>State estimation</subject><isbn>0769534953</isbn><isbn>9780769534954</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjL1OwzAURi2hSqWlIxNLXiDB1_8eqwhopFQM0Lm6qe1gREJkZ-nbEwTD0afzDYeQe6AVALWPTX1s9xWj1FSgb8iGamUlFwsrsvm9LTUS-Jrscv6klIJVGqS5JWUzYO-LN98Pfpxxjt9jgbloPaYxjn2x6OE6-dQnnD7yHVkF_Mp-979bcnp-eq8PZfv60tT7toyg5VwCMM-0cRqDNYypznFJhXHCKBWc5S4EZhR2XZCoGReMg5ICLx27gESp-ZY8_HWj9_48pThgup6FZmLp8R_kvkFc</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Lei Ding</creator><creator>Yilmaz, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>Image Segmentation as Learning on Hypergraphs</title><author>Lei Ding ; Yilmaz, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-112e278d7af98226bd35048d4866fd93dff286abbf5a7234231654acb2c15a573</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Application software</topic><topic>Computer vision</topic><topic>Humans</topic><topic>hypergraphs</topic><topic>Image segmentation</topic><topic>Iterative algorithms</topic><topic>Laplace equations</topic><topic>Laplacian matrix</topic><topic>Learning systems</topic><topic>Machine learning</topic><topic>Pixel</topic><topic>State estimation</topic><toplevel>online_resources</toplevel><creatorcontrib>Lei Ding</creatorcontrib><creatorcontrib>Yilmaz, 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>Lei Ding</au><au>Yilmaz, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Image Segmentation as Learning on Hypergraphs</atitle><btitle>2008 Seventh International Conference on Machine Learning and Applications</btitle><stitle>ICMLA</stitle><date>2008-12</date><risdate>2008</risdate><spage>247</spage><epage>252</epage><pages>247-252</pages><isbn>0769534953</isbn><isbn>9780769534954</isbn><abstract>In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as the objects and background. Using the seed pixels, our method predicts the labels for all unlabeled pixels. We present the relations of the proposed method to other hypergraph based learning techniques. We give an adaptive procedure for constructing image hypergraphs and achieve promising results on a real image dataset.</abstract><pub>IEEE</pub><doi>10.1109/ICMLA.2008.17</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 0769534953 |
| ispartof | 2008 Seventh International Conference on Machine Learning and Applications, 2008, p.247-252 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_4724982 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Computer vision Humans hypergraphs Image segmentation Iterative algorithms Laplace equations Laplacian matrix Learning systems Machine learning Pixel State estimation |
| title | Image Segmentation as Learning on Hypergraphs |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T11%3A20%3A52IST&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=Image%20Segmentation%20as%20Learning%20on%20Hypergraphs&rft.btitle=2008%20Seventh%20International%20Conference%20on%20Machine%20Learning%20and%20Applications&rft.au=Lei%20Ding&rft.date=2008-12&rft.spage=247&rft.epage=252&rft.pages=247-252&rft.isbn=0769534953&rft.isbn_list=9780769534954&rft_id=info:doi/10.1109/ICMLA.2008.17&rft_dat=%3Cieee_6IE%3E4724982%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4724982&rfr_iscdi=true |