Texture classification using an invariant texture representation and a tree matching kernel
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2010
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eng
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xxi, 233 leaves : ill. ; 30 cm.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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National Institute of Development Administration. Library and Information Center
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Somkid Soottitantawat (2010). Texture classification using an invariant texture representation and a tree matching kernel. Retrieved from: http://repository.nida.ac.th/handle/662723737/282.
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Texture classification using an invariant texture representation and a tree matching kernel
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Abstract
The real world is rich in many textures, which can be regarded as the visual
appearance of surfaces. They may be perceived as being smooth or rough, coarse or
fine, homogeneous or non-homogeneous, etc. Moreover, textures within real images
vary in scale, rotation and illumination. Several researchers have proposed texture
analysis methods to describe textures in many applications, such as computer vision,
pattern recognition, image retrieval, scene image analysis, and so on.
Although the analysis of texture properties has attracted the interest of
researchers for more than three decades, in this dissertation, an alternative approach
for texture classification using an invariant texture representation, tree-of-keypoints
and a tree matching kernel is proposed. The approach identifies regions of a given
texture image using Speed-Up Robust Feature or SURF descriptors. The regions of
the training texture images are then clustered into a tree of non-uniformly shaped
regions based on their distribution using a hierarchical k-means algorithm. The tree
structure forms a tree of key points which are used to determine the similarities
between two texture images. A similarity is computed based on an approximate
matching kernel called a tree matching kernel. Finally, Support Vector Machines
(SVMs) that utilize the tree matching kernel are constructed to classify textures. The
performance of the proposed method was evaluated through experiments performed
on textures from the Brodatz and UIUCTex datasets and, in all experiments with the
three learning schemes and different three weighting schemes, performed consistently
better than other previously reported methods.
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Thesis (Ph.D. (Computer Science))--National Institute of Development Administration, 2010