Dimensionality reduction-based feature extraction and classification on fleece fabric images

YILDIZ K.

SIGNAL IMAGE AND VIDEO PROCESSING, cilt.11, sa.2, ss.317-323, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 2
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s11760-016-0939-9
  • Dergi Adı: SIGNAL IMAGE AND VIDEO PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.317-323
  • Anahtar Kelimeler: Feature extraction, Classification, Dimensionality reduction, Principal component analysis, Local binary pattern, PRINCIPAL-COMPONENTS, TEXTURE, PATTERN, COLOR
  • Marmara Üniversitesi Adresli: Evet

Özet

This work performs dimensionality reduction-based classification on fleece fabric-based images taken by a thermal camera. In order to convert images into the gray level, a principal component analysis-based dimension reduction stage was proposed. In addition, symmetric central local binary patterns were performed with the help of the proposed method by using the images after dimension reduction process. The local binary pattern features preserve local texture features from different kinds of defective image types. The experimental results showed that combined work has a great classification accuracy. The classification accuracy was reported using two different algorithms: Naive Bayes and K-nearest neighbor classifier.