Efficient detection method for foreign fibers in cotton

Abstract Since foreign fibers in cotton seriously affect the quality of the final cotton textile products, machine-vision-based detection systems for foreign fibers in cotton are receiving extensive attention in industrial equipment. As one of the key components in detection systems, the suitable and good classifier is significantly important for machine-vision-based on detection systems for foreign fibers in cotton due to it improving the system’s performance. In the study, we test five classifiers in the dataset of foreign fibers in cotton, and for finding the best feature set corresponding to the classifiers, we use the four filter feature selection approaches to find the best feature sets of foreign fibers in cotton corresponding to specific classifiers. The experimental results show that the extreme learning machine and kernel support vector machines have the excellent performance for foreign fiber detection and the classification accuracy are respectively 93.61% and 93.17% using the selected corresponding feature set with 42 and 52 features.

[1]  Zhen Zhang,et al.  Uncertain XML documents classification using Extreme Learning Machine , 2016, Neurocomputing.

[2]  Kaveh Pahlavan,et al.  Enlighten Wearable Physiological Monitoring Systems: On-Body RF Characteristics Based Human Motion Classification Using a Support Vector Machine , 2016, IEEE Transactions on Mobile Computing.

[3]  Chang-Hwan Lee.  A gradient approach for value weighted classification learning in naive Bayes , 2015, Knowl. Based Syst..

[4]  Chi-Man Vong,et al.  Local Receptive Fields Based Extreme Learning Machine , 2015, IEEE Computational Intelligence Magazine.

[5]  Daoliang Li,et al.  Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton , 2014, Appl. Soft Comput..

[6]  Daoliang Li,et al.  Fast recognition of foreign fibers in cotton lint using machine vision , 2011, Math. Comput. Model..

[7]  Daoliang Li,et al.  A new approach for image processing in foreign fiber detection , 2009 .

[8]  Daoliang Li,et al.  A two-stage feature selection method with its application , 2015, Comput. Electr. Eng..

[9]  John R. Stevens,et al.  Accounting for Dependence Induced by Weighted KNN Imputation in Paired Samples, Motivated by a Colorectal Cancer Study , 2015, PloS one.

[10]  Daoliang Li,et al.  Classification and identification of foreign fibers in cotton on the basis of a support vector machine , 2010, Math. Comput. Model..

[11]  Wei-Pang Yang,et al.  Classifier design with feature selection and feature extraction using layered genetic programming , 2008, Expert Syst. Appl..

[12]  Tzu-Tsung Wong,et al.  Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation , 2015, Pattern Recognit..

[13]  Fuchun Sun,et al.  Dynamic texture video classification using extreme learning machine , 2016, Neurocomputing.

[14]  Daoliang Li,et al.  Original paper: Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine , 2010 .

[15]  Chi-Man Vong,et al.  Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis , 2016, Neurocomputing.