INTELLIGENT UNDERSTANDING OF USER INTERACTION IN IMAGE SEGMENTATION

dc.creatorSpina, Thiago V.
dc.creatorDe Miranda, Paulo A. V.
dc.creatorFalcao, Alexandre X.
dc.date2012
dc.date2013-09-19T18:06:13Z
dc.date2016-06-30T18:12:33Z
dc.date2013-09-19T18:06:13Z
dc.date2016-06-30T18:12:33Z
dc.date.accessioned2018-03-29T01:52:56Z
dc.date.available2018-03-29T01:52:56Z
dc.identifierInternational Journal of Pattern Recognition and Artificial Intelligence. World Scientific Publ Co Pte Ltd, v.26, n.2, 2012
dc.identifier0218-0014
dc.identifierWOS:000308104300008
dc.identifier10.1142/S0218001412650016
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/1977
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/1977
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1308277
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionWe have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problemduring delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated; fuzzy classification for arc-weight estimation; and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work.
dc.description26
dc.description2
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageeng
dc.publisherWorld Scientific Publ Co Pte Ltd
dc.publisherSingapore
dc.relationInternational Journal of Pattern Recognition and Artificial Intelligence
dc.rightsfechado
dc.sourceWOS
dc.subjectGraph-based image segmentation
dc.subjectintelligent arc-weight estimation
dc.subjectimage foresting transform
dc.subjectfuzzy classification
dc.subjectclustering
dc.subjectOPTIMUM-PATH FOREST
dc.subjectRELATIVE FUZZY CONNECTEDNESS
dc.subjectGRAPH CUTS
dc.subjectMULTIPLE OBJECTS
dc.subjectLIVE WIRE
dc.subjectALGORITHMS
dc.titleINTELLIGENT UNDERSTANDING OF USER INTERACTION IN IMAGE SEGMENTATION
dc.typeArtículos de revistas


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