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Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis Open Access


Other title
scale space
multiple kernel learning
oil sand image analysis
Type of item
Degree grantor
University of Alberta
Author or creator
Nilufar, Sharmin
Supervisor and department
Nilanjan Ray (Computing Science)
Examining committee member and department
Nilanjan Ray (Computing Science)
Guohui Lin (Computing Science)
Sriraam Natarajan (Computer Science)
Hong Zhang (Computing Science)
Vicky Zhao (Electrical and Computer engineering)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Scale-space representation for an image is a significant way to generate features for object detection/classification. The size of the object we are looking for as well as its texture contents are related to the multi-scale representations. However, any scale-space based features face the inevitable issues of high dimentionality and scale selection. Scale-space analysis of image provides a set of extremely high dimensional features at each scale- the number of pixels in a filtered output image is the feature dimensionality at that scale. Moreover, considering all the output images at various scales, the dimensionality of the feature set is staggeringly high. Selection of features from this high dimensional space is daunting. In addition, the scale selection process is still ad-hoc, while applying scale-space based features for object detection/classification. In this research these two issues are resolved by designing a suitable kernel function on the scale space based features and applying multiple kernel learning (MKL) approach for sparse selection of scales. A novel shift invariant kernel function for scale space based features is designed here. Also a novel framework for multiple kernel learning is proposed that utilizes a 1-norm support vector machine (SVM) in the MKL optimization problem for sparse selection and weighting of scales from scale-space. The optimized data-dependent kernel accommodates only a few scales that are most discriminatory according to the large margin principle. With a 2-norm SVM this learned kernel is applied to the classification problem. In this thesis we have applied the proposed classification method for oil sand image analysis. Automatic analysis of oil sand video images is non-trivial due to the presence of dirt and fine materials. In addition, changeable weather and lighting condition make the video quality worse. Two challenging problems in oil sand mining which are detection of large lump and steam from videos are investigated here. Difference of Gaussian (DoG) and wavelet scale space are applied for these two different detection problems, respectively. Our method yields encouraging results on these difficult-to-process video images and compares favourably against other existing methods.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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