Multi-Scale Shape Index-Based Local Binary Patterns for Texture Classification

ALPASLAN N., Hanbay K.

IEEE SIGNAL PROCESSING LETTERS, cilt.27, ss.660-664, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1109/lsp.2020.2987474
  • Dergi Adı: IEEE SIGNAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.660-664
  • Anahtar Kelimeler: Shape, Indexes, Feature extraction, Kernel, Lighting, Windows, Histograms, Feature representation, local binary pattern, shape index, texture descriptors, FACE, DESCRIPTORS, MODELS
  • İnönü Üniversitesi Adresli: Hayır

Özet

To enhance the weakness of Local Binary Pattern (LBP) and its state-of-the-art variants, this letter presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index based texture descriptor is named as SI-LCvMSP. Contrarily to the original LBP and LCvMSP, SI-LCvMSP uses the shape index instead of the original texture image in the kernel function. The shape index is a differential calculation and it can be calculated from local second-order derivatives of texture images. It captures microstructure and macrostructure texture information mathematically. As textural features, we use multi-scale and multi-resolution shape index information as well as rotation-invariant uniform LBP. Thus, we obtain the discriminative feature representation schema to construct cross-scale joint coding. The proposed method has a high discriminability and is less sensitive to image transforms such as rotation and illumination. Experimental results show that the SI-LCvMSP descriptor can improve classification accuracy.