Scale and rotation invariant texture classification using covariate shift methodology

Hassan, Ali, Riaz, Farhan and Shaukat, Arslan (2014) Scale and rotation invariant texture classification using covariate shift methodology. IEEE Signal Processing Letters, 21 (3). pp. 321-324. ISSN 1070-9908

Full content URL: 10.1109/LSP.2014.2302576

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Item Type:Article
Item Status:Live Archive


In this letter, we propose to tackle rotation and scale variance in texture classification at the machine learning level. This is achieved by using image descriptors that interpret these variations as shifts in the feature vector. We model these variations as a covariate shift in the data. This shift is then reduced by minimising the Kullback–Leibler divergence between the true and estimated distributions using importance weights (IW). These IWs are used in support vector machines (SVMs) to formulate the IW-SVMs.
The experimental results show that IW-SVMs exhibit good invariance characteristics and outperform other state-of-the-art classification methods. The proposed methodology gives a generic solution that can be applied to any texture descriptor that models the transformations as a shift in the feature vector.

Keywords:Feature Extraction, Training, Testing, Standards, Support vector machines, Machine learning algorithms, Vectors
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52396
Deposited On:18 Nov 2022 11:36

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