Classification and identification of foreign fibers in cotton on the basis of a support vector machine

Authors:
Ronghua Ji;Daoliang Li;Lairong Chen;Wenzhu Yang
Affiliations:
College of Information and Electronic Engineering, China Agricultural University, Beijing, 100083, China;College of Information and Electronic Engineering, China Agricultural University, Beijing, 100083, China;School of Technology, Beijing Forestry University, Beijing, 100083, China;College of Information and Electronic Engineering, China Agricultural University, Beijing, 100083, China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2010

Citing 3
Cited 1

Machine vision for automated visual inspection of cotton quality in textile industries using color isodiscrimination contour

Computers and Industrial Engineering
Mean shift algorithm equipped with the intersection of confidence intervals rule for image segmentation

Pattern Recognition Letters
The estimation of the gradient of a density function, with applications in pattern recognition

IEEE Transactions on Information Theory

Learning SVM with weighted maximum margin criterion for classification of imbalanced data

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.98

Visualization

Abstract

Foreign fibers in cotton have seriously affected the quality of cotton products. The classification and identification of foreign fibers in cotton is the foundation of cotton foreign fiber automated inspection. The paper takes the typical cotton foreign fibers in China's textile enterprises as the research object, and acquires the images under simulated actual cotton processing. The various classification features are calculated and analyzed. The results show that aspect ratio, roundness, duty cycle and I"1 are the effective features for classifying various foreign fibers. The paper puts forward a classifier of cotton foreign fibers based on a support vector machine. A Decision Tree Support Vector Machine (DTSVM) can not only avoid the non-separated region, but also improve the training speed while the training sample number gradually decreases, going through the decision tree. The DTSVM is to be used to identify the sorts of common foreign fibers in cotton. The experimental data set shows that the rates of identifying different kinds of foreign fibers are greater than 92% using the proposed DTSVM.