Dimensionality reduction-based feature extraction and classification on fleece fabric images

详细信息    查看全文

• 作者：Kazim Yildiz
• 关键词：Feature extraction ; Classification ; Dimensionality reduction ; Principal component analysis ; Local binary pattern
• 刊名：Signal, Image and Video Processing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：11
• 期：2
• 页码：317-323
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems;
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：11

文摘

This work performs dimensionality reduction-based classification on fleece fabric-based images taken by a thermal camera. In order to convert images into the gray level, a principal component analysis-based dimension reduction stage was proposed. In addition, symmetric central local binary patterns were performed with the help of the proposed method by using the images after dimension reduction process. The local binary pattern features preserve local texture features from different kinds of defective image types. The experimental results showed that combined work has a great classification accuracy. The classification accuracy was reported using two different algorithms: Naive Bayes and K-nearest neighbor classifier.