An integrated approach for scene understanding based on Markov Random Field model

Cited 6 time in webofscience Cited 0 time in scopus
  • Hit : 268
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, IY (Kim, IY)ko
dc.contributor.authorYang, Hyun-Seungko
dc.date.accessioned2013-03-02T18:18:25Z-
dc.date.available2013-03-02T18:18:25Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1995-12-
dc.identifier.citationPATTERN RECOGNITION, v.28, no.12, pp.1887 - 1897-
dc.identifier.issn0031-3203-
dc.identifier.urihttp://hdl.handle.net/10203/74867-
dc.description.abstractIn this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segmented into a set of disjoint regions by a conventional region-based segmentation technique which operates on image pixels, and a Region Adjacency Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our scheme then proceeds on the RAG by defining the region merging and labeling problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function. We exploit the proposed scheme to segment and interpret natural outdoor scene images.-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.subjectSEGMENTATION-
dc.titleAn integrated approach for scene understanding based on Markov Random Field model-
dc.typeArticle-
dc.identifier.wosidA1995TM23100008-
dc.identifier.scopusid2-s2.0-0029515540-
dc.type.rimsART-
dc.citation.volume28-
dc.citation.issue12-
dc.citation.beginningpage1887-
dc.citation.endingpage1897-
dc.citation.publicationnamePATTERN RECOGNITION-
dc.contributor.localauthorYang, Hyun-Seung-
dc.contributor.nonIdAuthorKim, IY (Kim, IY)-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorEnergy function-
dc.subject.keywordAuthorMarkov random field-
dc.subject.keywordAuthorRegion adjacency graph-
dc.subject.keywordAuthorRegion clusters-
dc.subject.keywordAuthorRegion labeling-
dc.subject.keywordAuthorSimulated annealing-
dc.subject.keywordPlusSEGMENTATION-
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 6 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0