Object co-skeletonization with co-segmentation

Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu, Junsong Yuan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

29 Citations (Scopus)


Recent advances in the joint processing of images have certainly shown its advantages over the individual processing. Different from the existing works geared towards cosegmentation or co-localization, in this paper, we explore a new joint processing topic: co-skeletonization, which is defined as joint skeleton extraction of common objects in a set of semantically similar images. Object skeletonization in real world images is a challenging problem, because there is no prior knowledge of the object's shape if we consider only a single image. This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help, just as it does for other joint processing problems such as cosegmentation. Noting that skeleton can provide good scribbles for segmentation, and skeletonization, in turn, needs good segmentation, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically. Since it is a new problem, we also construct a benchmark dataset for the co-skeletonization task. Extensive experiments demonstrate that proposed method achieves very competitive results.

Original languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
EditorsYanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781538604571
ISBN (Print)9781538604588
Publication statusPublished - 2017
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2017 - Honolulu, United States of America
Duration: 21 Jul 201726 Jul 2017
https://ieeexplore.ieee.org/xpl/conhome/8097368/proceeding (Proceedings)


ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2017
Abbreviated titleCVPR 2017
CountryUnited States of America
Internet address

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