Rotation Invariant Local Frequency Descriptors for Texture Analysis

• Author(s) / Creator(s)

  • Yang, Yee-Hong (Herb)
  • Maani, Rouzbeh
  • Kalra, Sanjay

• This paper presents a novel, simple, yet powerful texture analysis method inspired by the well-known Local Binary Patterns (LBP) method called the Local Frequency Descriptors (LFD). Like LBP, the proposed method is invariant to rotation and linear changes of illumination; however,it does not suffer from the limitations of LBP such as exponential growth of features with an increment in the number of neighbors. The experimental results on the Outex and CUReT datasets show that the proposed LFD method outperforms state-of-the-art texture analysis methods. In addition, LFD is very robust to noise and can improve LBP results up to 50% in extremely noisy conditions. In this paper, we discuss different aspects of the LFD and explain how it addresses the main limitations of LBP and of its variants. | TRID-ID TR12-03

• Date created

  2012

• Subjects / Keywords

  • Local binary patterns
  • Computer Vision and Multimedia Communications
  • Texture analysis
  • Image processing
  • Local frequency descriptors
  • Feature analysis

• Type of Item

  Report

• DOI

  https://doi.org/10.7939/R3GM81Q08

• License

  Attribution 3.0 International