Open AccessA Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification
Author(s) -
Jianning Chi,
Xiaoqing Yu,
Yifei Zhang,
Huan Wang
Publication year - 2019
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2019/3756048
Subject(s) - pattern recognition (psychology) , artificial intelligence , principal component analysis , feature (linguistics) , computer science , texture (cosmology) , set (abstract data type) , feature selection , perception , image (mathematics) , feature vector , key (lock) , selection (genetic algorithm) , mathematics , philosophy , linguistics , neuroscience , biology , programming language , computer security
This paper proposes a novel local texture description method which defines six human visual perceptual characteristics and selects the minimal subset of relevant as well as nonredundant features based on principal component analysis (PCA). We assign six texture characteristics, which were originally defined by Tamura et al., with novel definition and local metrics so that these measurements reflect the human perception of each characteristic more precisely. Then, we propose a PCA-based feature selection method exploiting the structure of the principal components of the feature set to find a subset of the original feature vector, where the features reflect the most representative characteristics for the textures in the given image dataset. Experiments on different publicly available large datasets demonstrate that the proposed method provides superior performance of classification over most of the state-of-the-art feature description methods with respect to accuracy and efficiency.