DocumentCode :
2190431
Title :
Covariate shift approach for invariant texture classification
Author :
Hassan, Asif ; Shaukat, Arslan
Author_Institution :
Dept. of Comput. Eng., Nat. Univ. of Sci. & Technol. (NUST), Islamabad, Pakistan
fYear :
2013
fDate :
22-25 Sept. 2013
Firstpage :
1
Lastpage :
6
Abstract :
This paper deals with rotation and scale invariant texture classification problem at the machine learning level by modelling these variations in the texture data as a covariate shift. Covariate shift between the training and testing data is minimised by estimating importance weights for the training data which are then incorporated in a standard machine learning algorithm like support vector machines. The effectiveness of these importance weighted support vector machines (IW-SVM) are tested on the Brodatz dataset. The comparative classification results with several other state of the art methodologies demonstrate the effectiveness of the proposed covariate shift approach for rotation and scale invariant texture classification.
Keywords :
covariance analysis; image classification; image texture; learning (artificial intelligence); support vector machines; Brodatz dataset; IW-SVM; comparative classification; covariate shift approach; importance weighted support vector machines; machine learning algorithm; machine learning level; rotation invariant texture classification; scale invariant texture classification; state of the art methodology; testing data; texture data; training data; Accuracy; Standards; Support vector machines; Testing; Training; Training data; Vectors; Machine learning; covariate shift; importance weighting and support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on
Conference_Location :
Southampton
ISSN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2013.6661945
Filename :
6661945
Link To Document :
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