Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition

Piotr Koniusz, Anoop Cherian

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

    26 Citations (Scopus)

    Abstract

    Super-symmetric tensors - a higher-order extension of scatter matrices - are becoming increasingly popular in machine learning and computer vision for modeling data statistics, co-occurrences, or even as visual descriptors. They were shown recently to outperform second-order approaches [18], however, the size of these tensors are exponential in the data dimensionality, which is a significant concern. In this paper, we study third-order supersymmetric tensor descriptors in the context of dictionary learning and sparse coding. For this purpose, we propose a novel non-linear third-order texture descriptor. Our goal is to approximate these tensors as sparse conic combinations of atoms from a learned dictionary. Apart from the significant benefits to tensor compression that this framework offers, our experiments demonstrate that the sparse coefficients produced by this scheme lead to better aggregation of high-dimensional data and showcase superior performance on two common computer vision tasks compared to the state of the art.

    Original languageEnglish
    Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
    PublisherIEEE Computer Society
    Pages5395-5403
    Number of pages9
    ISBN (Electronic)9781467388504
    DOIs
    Publication statusPublished - 9 Dec 2016
    Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
    Duration: 26 Jun 20161 Jul 2016

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume2016-December
    ISSN (Print)1063-6919

    Conference

    Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
    Country/TerritoryUnited States
    CityLas Vegas
    Period26/06/161/07/16

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