Sparse Variation Pattern for Texture Classification

M. Tavakolian, F. Hajati, Ajmal Mian, S. Gheisari

    Research output: Chapter in Book/Conference paperConference paper

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    Abstract

    We present Sparse Variation Pattern (SVP) to extract image features for texture classification. Using the directional derivatives in a local circular neighborhood, SVP captures texture transition patterns in the spatial domain. Unlike conventional feature extraction methods, SVP characterizes the image points taking the co-occurrence of two derivatives in the same direction into account without encoding to binary patterns. Using the directional derivatives, SVP defines a dictionary to solve the classification problem with sparse representation. The proposed texture descriptor was evaluated on the FERET and the LFW face databases, and the PolyU palmprint database. Comparisons with the existing state-of-the-art methods demonstrate that the SVP achieves the overall best performance on all three databases.
    Original languageEnglish
    Title of host publication2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-6
    ISBN (Print)9781479921263
    DOIs
    Publication statusPublished - Nov 2013
    Event2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Hobart, TAS, Hobart, Australia
    Duration: 26 Nov 201328 Nov 2013

    Conference

    Conference2013 International Conference on Digital Image Computing
    CountryAustralia
    CityHobart
    Period26/11/1328/11/13

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    Textures
    Derivatives
    Glossaries
    Feature extraction

    Cite this

    Tavakolian, M., Hajati, F., Mian, A., & Gheisari, S. (2013). Sparse Variation Pattern for Texture Classification. In 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-6). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2013.6691530
    Tavakolian, M. ; Hajati, F. ; Mian, Ajmal ; Gheisari, S. / Sparse Variation Pattern for Texture Classification. 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1-6
    @inproceedings{b86354ba729942eb8fab16b13886e2bf,
    title = "Sparse Variation Pattern for Texture Classification",
    abstract = "We present Sparse Variation Pattern (SVP) to extract image features for texture classification. Using the directional derivatives in a local circular neighborhood, SVP captures texture transition patterns in the spatial domain. Unlike conventional feature extraction methods, SVP characterizes the image points taking the co-occurrence of two derivatives in the same direction into account without encoding to binary patterns. Using the directional derivatives, SVP defines a dictionary to solve the classification problem with sparse representation. The proposed texture descriptor was evaluated on the FERET and the LFW face databases, and the PolyU palmprint database. Comparisons with the existing state-of-the-art methods demonstrate that the SVP achieves the overall best performance on all three databases.",
    author = "M. Tavakolian and F. Hajati and Ajmal Mian and S. Gheisari",
    year = "2013",
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    doi = "10.1109/DICTA.2013.6691530",
    language = "English",
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    Tavakolian, M, Hajati, F, Mian, A & Gheisari, S 2013, Sparse Variation Pattern for Texture Classification. in 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 1-6, 2013 International Conference on Digital Image Computing, Hobart, Australia, 26/11/13. https://doi.org/10.1109/DICTA.2013.6691530

    Sparse Variation Pattern for Texture Classification. / Tavakolian, M.; Hajati, F.; Mian, Ajmal; Gheisari, S.

    2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1-6.

    Research output: Chapter in Book/Conference paperConference paper

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    AB - We present Sparse Variation Pattern (SVP) to extract image features for texture classification. Using the directional derivatives in a local circular neighborhood, SVP captures texture transition patterns in the spatial domain. Unlike conventional feature extraction methods, SVP characterizes the image points taking the co-occurrence of two derivatives in the same direction into account without encoding to binary patterns. Using the directional derivatives, SVP defines a dictionary to solve the classification problem with sparse representation. The proposed texture descriptor was evaluated on the FERET and the LFW face databases, and the PolyU palmprint database. Comparisons with the existing state-of-the-art methods demonstrate that the SVP achieves the overall best performance on all three databases.

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    Tavakolian M, Hajati F, Mian A, Gheisari S. Sparse Variation Pattern for Texture Classification. In 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1-6 https://doi.org/10.1109/DICTA.2013.6691530