Original paper: Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine

Authors:
Daoliang Li;Wenzhu Yang;Sile Wang
Affiliations:
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China;College of Mathematics and Computer Science, Hebei University, Baoding 071002, PR China and College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR Chi ...;College of Mathematics and Computer Science, Hebei University, Baoding 071002, PR China
Venue:
Computers and Electronics in Agriculture
Year:
2010

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Abstract

Automatic classification of foreign fibers in cotton lint using machine vision is still a challenge due to various colors and shapes of the foreign fibers. This paper presents a novel classification method based on multi-class support vector machine (MSVM) which aims at accurate and fast classification of the foreign fibers. Firstly, live images were acquired by a machine vision system and then processed using image processing algorithms. Then the color features, shape features and texture features of each foreign fiber object were extracted and feature vectors were composed. Afterwards, three kinds of multi-class support vector machines were constructed, i.e., one-against-all decision-tree based MSVM, one-against-one voting based MSVM and one-against-one directed acyclic graph MSVM separately. At last, with the extracted feature vectors as input, the MSVMs were tested using leave-one-out cross validation. The results indicate that both the one-against-one voting based MSVM and the one-against-one directed acyclic graph MSVM can satisfy the accuracy requirement of the classification of foreign fibers, and the mean accuracy is 93.57% and 92.34% separately. The one-against-all decision-tree based MSVM only obtains mean accuracy of 79.25% which can not meet the accuracy requirement. In classification speed, one-against-one directed acyclic graph MSVM is the fastest and fitter for online classification.