Material classification on symmetric positive definite manifolds

Masoud Faraki, Mehrtash T. Harandi, Fatih Porikli

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

    13 Citations (Scopus)

    Abstract

    This paper tackles the problem of categorizing materials and textures by exploiting the second order statistics. To this end, we introduce the Extrinsic Vector of Locally Aggregated Descriptors (E-VLAD), a method to combine local and structured descriptors into a unified vector representation where each local descriptor is a Covariance Descriptor (CovD). In doing so, we make use of an accelerated method of obtaining a visual codebook where each atom is itself a CovD. We will then introduce an efficient way of aggregating local CovDs into a vector representation. Our method could be understood as an extrinsic extension of the highly acclaimed method of Vector of Locally Aggregated Descriptors [17] (or VLAD) to CovDs. We will show that the proposed method is extremely powerful in classifying materials/ textures and can outperform complex machineries even with simple classifiers.

    Original languageEnglish
    Title of host publicationProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages749-756
    Number of pages8
    ISBN (Electronic)9781479966820
    DOIs
    Publication statusPublished - 19 Feb 2015
    Event2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States
    Duration: 5 Jan 20159 Jan 2015

    Publication series

    NameProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015

    Conference

    Conference2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
    Country/TerritoryUnited States
    CityWaikoloa
    Period5/01/159/01/15

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