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Incorporating Two First Order Moments into LBP-Based Operator for Texture Categorization

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

Within different techniques for texture modelling and recognition, local binary patterns and its variants have received much interest in recent years thanks to their low computational cost and high discrimination power. We propose a new texture description approach, whose principle is to extend the LBP representation from the local gray level to the regional distribution level. The region is represented by pre-defined structuring element, while the distribution is approximated using the two first statistical moments. Experimental results on four large texture databases, including Outex, KTH-TIPS 2b, CUReT and UIUC show that our approach significantly improves the performance of texture representation and classification with respect to comparable methods.

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Notes

  1. 1.

    Note that a moment image corresponds to a local filter defined by a statistical moment, and should not be confused with the concept of “image moment”.

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Author information

Authors and Affiliations

  1. ENSTA-ParisTech, 828 Boulevard des Maréchaux, 91762, Palaiseau, France

    Thanh Phuong Nguyen & Antoine Manzanera

Authors
  1. Thanh Phuong Nguyen
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  2. Antoine Manzanera
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Corresponding author

Correspondence to Thanh Phuong Nguyen .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Nguyen, T.P., Manzanera, A. (2015). Incorporating Two First Order Moments into LBP-Based Operator for Texture Categorization. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_38

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

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