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dc.contributor.authorWang, P.en
dc.contributor.authorShen, C.en
dc.contributor.authorVan Den Hengel, A.en
dc.identifier.citationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015 / vol.07-12-June-2015, pp.3222-3231en
dc.description.abstractConditional Random Fields (CRFs) are one of the core technologies in computer vision, and have been applied to a wide variety of tasks. Conventional CRFs typically define edges between neighboring image pixels, resulting in a sparse graph over which inference can be performed efficiently. However, these CRFs fail to model more complex priors such as long-range contextual relationships. Fullyconnected CRFs have thus been proposed. While there are efficient approximate inference methods for such CRFs, usually they are sensitive to initialization and make strong assumptions. In this work, we develop an efficient, yet general SDP algorithm for inference on fully-connected CRFs. The core of the proposed algorithm is a tailored quasi- Newton method, which solves a specialized SDP dual problem and takes advantage of the low-rank matrix approximation for fast computation. Experiments demonstrate that our method can be applied to fully-connected CRFs that could not previously be solved, such as those arising in pixel-level image co-segmentation.en
dc.description.statementofresponsibilityPeng Wang, Chunhua Shen, Anton van den Hengelen
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognitionen
dc.rightsCopyright © 2015, IEEEen
dc.titleEfficient SDP inference for fully-connected CRFs based on low-rank decompositionen
dc.typeConference paperen
dc.contributor.conferenceConference on Computer Vision and Pattern Recognition (CVPR) (07 Jun 2015 - 12 Jun 2015 : Boston, MA)en
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidShen, C. [0000-0002-8648-8718]en
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]en
Appears in Collections:Computer Science publications

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