DenseCut: densely connected CRFs for realtime GrabCut

Figure-ground segmentation from bounding box input, provided either automatically or manually, has been extremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques...

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Main Authors: Cheng, MM, Prisacariu, VA, Zheng, S, Torr, PHS, Rother, C
Formato: Journal article
Idioma:English
Publicado: Wiley 2015
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author Cheng, MM
Prisacariu, VA
Zheng, S
Torr, PHS
Rother, C
author_facet Cheng, MM
Prisacariu, VA
Zheng, S
Torr, PHS
Rother, C
author_sort Cheng, MM
collection OXFORD
description Figure-ground segmentation from bounding box input, provided either automatically or manually, has been extremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practical usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected crf. To motivate this decision, we show that a dense crf implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense crf and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10×) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.
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spelling oxford-uuid:a3c3f6b0-63bc-4a51-a387-df5d13ca294f2024-05-17T10:57:15ZDenseCut: densely connected CRFs for realtime GrabCutJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a3c3f6b0-63bc-4a51-a387-df5d13ca294fEnglishSymplectic ElementsWiley2015Cheng, MMPrisacariu, VAZheng, STorr, PHSRother, CFigure-ground segmentation from bounding box input, provided either automatically or manually, has been extremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practical usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected crf. To motivate this decision, we show that a dense crf implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense crf and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10×) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.
spellingShingle Cheng, MM
Prisacariu, VA
Zheng, S
Torr, PHS
Rother, C
DenseCut: densely connected CRFs for realtime GrabCut
title DenseCut: densely connected CRFs for realtime GrabCut
title_full DenseCut: densely connected CRFs for realtime GrabCut
title_fullStr DenseCut: densely connected CRFs for realtime GrabCut
title_full_unstemmed DenseCut: densely connected CRFs for realtime GrabCut
title_short DenseCut: densely connected CRFs for realtime GrabCut
title_sort densecut densely connected crfs for realtime grabcut
work_keys_str_mv AT chengmm densecutdenselyconnectedcrfsforrealtimegrabcut
AT prisacariuva densecutdenselyconnectedcrfsforrealtimegrabcut
AT zhengs densecutdenselyconnectedcrfsforrealtimegrabcut
AT torrphs densecutdenselyconnectedcrfsforrealtimegrabcut
AT rotherc densecutdenselyconnectedcrfsforrealtimegrabcut