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Unconstrained texture classification using efficient jet texton learning
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DOI：10.1016/j.asoc.2019.105910.png)
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摘要
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This paper proposes a simple and effective texture recognition method that uses a new class of jet texton learning. In this approach, first a Jet space representation of the image is derived from a set of derivative of Gaussian (DtGs) filter responses upto 2nd order (R-6), so called local jet vector (LJV), which satisfies the scale space properties, where the combinations of local jets preserve the intrinsic local structure of the image in a hierarchical way and are invariant to image translation, rotation and scaling. Next, the jet textons dictionary is learned using K-means clustering algorithm from DtGs responses, followed by a contrast Weber law normalization pre-processing step. Finally, the feature distribution of jet texton is considered as a model which is utilized to classify texture using a non-parametric nearest regularized subspace (Nrs) classifier. Extensive experiments on three large and well-known benchmark database for texture classification like KTH-TIPS, Brodatz and CUReT show that the proposed method achieves state-of-the-art performance, especially when the number of available training samples is limited. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Derivative of Gaussian (DtG)
Jet texton learning
Local jet vector (LJV)
Texture classification
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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Representing and recognizing the visual appearance of materials using three-dimensional textons
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