A Comparison of Classification Methods for Local Binary Patterns


Kazak N., KOÇ M., BENLİGİRAY B., TOPAL C.

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

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

Abstract

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.