A comparison of classification methods for local binary patterns Yerel Ikili Örüntüler Için Siniflandirma Yöntemlerinin Karşilaştirilmasi

KAZAK ÇERÇEVİK N., Koc M., Benligiray B., Topal C.

24th Signal Processing and Communication Application Conference, SIU 2016, Zonguldak, Turkey, 16 - 19 May 2016, pp.805-808 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2016.7495862
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.805-808
  • Keywords: classification methods, local binary patterns, texture classification, UIUC texture database
  • Istanbul Technical University Affiliated: Yes


Texture recognition is an important tool used for content-based image retrieval, face recognition, and satellite image classification applications. One of the most successful features for texture recognition is local binary patterns (LBP), which computes local intensity differences for a pixel with respect to its neighbor pixels. In many studies in the literature, histogram based similarity measures are employed to classify LBP features. In this study, we investigate the performance of support vector machines, linear discriminant analysis, and linear regression classifier to improve the success of LBP features. We achieved 84.4% classification success using linear regression classification.