Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44106
Title: Decoupled marginal distribution of gradient magnitude and laplacian of gaussian for texture classification
Authors: Xue, W
Mou, X
Zhang, L 
Keywords: Decoupled marginal distributions
Gradient magnitude
Laplacian of gaussian
Texture classification
Issue Date: 2015
Publisher: Springer
Source: Communications in computer and information science, 2015, v. 546, p. 418-428 How to cite?
Journal: Communications in computer and information science 
Abstract: We propose a novel descriptor for classification of texture images based on two isotropic low level features: the gradient magnitude (GM) and the Laplacian of Gaussian (LOG). The local descriptor is devised as the concatenation of the marginal distributions and a decoupled marginal distributions of the two features in local patch. The isotropic low level features and the computation of the two distributions ensure the rotation invariance and its robustness. To make the descriptors contrast invariant, within each image and across difference images of the same class, L2-normalization and Weber normalization are implied to the two features. After examined on three benchmark datasets, the proposed descriptor is showed to be more effective than other filter bank based features. Besides, the proposed descriptor can achieve very good performance even with small patch.
Description: 1st Chinese Conference on Computer Vision, CCCV 2015, 18-20 September 2015
URI: http://hdl.handle.net/10397/44106
ISBN: 978-3662485576
ISSN: 1865-0929
DOI: 10.1007/978-3-662-48558-3_42
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