1. Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids
- Author
-
Tong Liu, Ziting Zhao, and Xudong Zhao
- Subjects
Article Subject ,General Computer Science ,Computer science ,General Mathematics ,Computer applications to medicine. Medical informatics ,R858-859.7 ,0211 other engineering and technologies ,Neurosciences. Biological psychiatry. Neuropsychiatry ,020101 civil engineering ,Feature selection ,Computational intelligence ,02 engineering and technology ,0201 civil engineering ,Machine Learning ,Gabor filter ,Image texture ,Artificial Intelligence ,021105 building & construction ,Classifier (linguistics) ,Interpretability ,business.industry ,General Neuroscience ,Pattern recognition ,General Medicine ,Inspection time ,Feature (computer vision) ,Artificial intelligence ,business ,Algorithms ,RC321-571 ,Research Article - Abstract
Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.
- Published
- 2021