Shape interaction matrix revisited and robustified: Efficient subspace clustering with corrupted and incomplete data

Pan Ji, Mathieu Salzmann, Hongdong Li

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

    55 Citations (Scopus)

    Abstract

    The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i.e., separating points drawn from a union of subspaces). In this paper, we revisit the SIM and reveal its connections to several recent subspace clustering methods. Our analysis lets us derive a simple, yet effective algorithm to robustify the SIM and make it applicable to realistic scenarios where the data is corrupted by noise. We justify our method by intuitive examples and the matrix perturbation theory. We then show how this approach can be extended to handle missing data, thus yielding an efficient and general subspace clustering algorithm. We demonstrate the benefits of our approach over state-of-the-art subspace clustering methods on several challenging motion segmentation and face clustering problems, where the data includes corruptions and missing measurements.

    Original languageEnglish
    Title of host publication2015 International Conference on Computer Vision, ICCV 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4687-4695
    Number of pages9
    ISBN (Electronic)9781467383912
    DOIs
    Publication statusPublished - 17 Feb 2015
    Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
    Duration: 11 Dec 201518 Dec 2015

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    Volume2015 International Conference on Computer Vision, ICCV 2015
    ISSN (Print)1550-5499

    Conference

    Conference15th IEEE International Conference on Computer Vision, ICCV 2015
    Country/TerritoryChile
    CitySantiago
    Period11/12/1518/12/15

    Fingerprint

    Dive into the research topics of 'Shape interaction matrix revisited and robustified: Efficient subspace clustering with corrupted and incomplete data'. Together they form a unique fingerprint.

    Cite this