TY - GEN
T1 - Feature-based transfer learning to train a novel cotton imaging system
AU - Shahriar, Muneem
AU - Sari-Sarraf, Hamed
AU - Hequet, Eric
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - In recent years, the transfer learning framework has gained increasing interest in the machine learning community. Fundamentally, this framework aims to train a new target system using existing data or knowledge from one or more previous source systems. By extending the theory of standard machine learning techniques, this framework allows us to solve many challenging problems directly and intuitively. This paper presents an application of this framework to train a novel target system whose goal is to measure a cotton fiber property named maturity using image analysis. In addition, this paper also presents a feature-based supervised domain adaptation approach named G2DA which performs mapping using the generalized (kernel) discriminant analysis. After domain adaptation is complete, model estimation is performed easily using traditional machine learning algorithms. Specifically, RANSAC-based regression is performed to learn a maturity function for the target system. This function is then used to estimate the maturity of any newly scanned fiber. Validation studies performed show good results for our overall approach.
AB - In recent years, the transfer learning framework has gained increasing interest in the machine learning community. Fundamentally, this framework aims to train a new target system using existing data or knowledge from one or more previous source systems. By extending the theory of standard machine learning techniques, this framework allows us to solve many challenging problems directly and intuitively. This paper presents an application of this framework to train a novel target system whose goal is to measure a cotton fiber property named maturity using image analysis. In addition, this paper also presents a feature-based supervised domain adaptation approach named G2DA which performs mapping using the generalized (kernel) discriminant analysis. After domain adaptation is complete, model estimation is performed easily using traditional machine learning algorithms. Specifically, RANSAC-based regression is performed to learn a maturity function for the target system. This function is then used to estimate the maturity of any newly scanned fiber. Validation studies performed show good results for our overall approach.
KW - domain adaptation
KW - non-destructive cotton evaluation
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=84862859821&partnerID=8YFLogxK
U2 - 10.1109/SSIAI.2012.6202486
DO - 10.1109/SSIAI.2012.6202486
M3 - Conference contribution
AN - SCOPUS:84862859821
SN - 9781467318303
T3 - Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
SP - 193
EP - 196
BT - 2012 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2012, Proceedings
Y2 - 22 April 2012 through 24 April 2012
ER -