Научная визуализация, 2019, том 11, номер 5, страницы 12 - 25, DOI: 10.26583/sv.11.5.02
Fusion of motif co-occurrence matrix and local binary pattern based on intuitionistic fuzzy set for texture classification
Автор: C.-P. Yen1
Department of Information Management, Central Police University
1 ORCID: 0000-0002-1189-4922, peter@mail.cpu.edu.tw
Аннотация
Texture classification plays an important role in computer vision and has a wide variety of applications. Based on intuitionistic fuzzy set (IFS) theory, this paper proposes a novel feature descriptor for texture classification by the fusion of motif co-occurrence matrix (MCM) and local binary pattern (LBP), namely IFS-MCMLBP. In this way, IFS is used to model vagueness or uncertainty, and the MCM method for extracting microtexture information, whilst the LBP method plays the role of a global feature. Intensive experiments conducted on many texture benchmarks such as CUReT, Outex, Brodatz and VisTex. The results show that the IFS-MCMLBP method can be remarkably superior to existing texture classification methods such as, LBP, GLCM, LTP, LDiP, LDeP and LTrP.
Ключевые слова: Intuitionistic fuzzy set (IFS), texture classification, motif co-occurrence matrix (MCM), local binary pattern (LBP).
