@inproceedings{14957de2fcd8487ea2cd0aff4aa36e01,
title = "Decoupled marginal distribution of gradient magnitude and laplacian of gaussian for texture classification",
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.",
keywords = "Decoupled marginal distributions, Gradient magnitude, Laplacian of gaussian, Texture classification",
author = "Wufeng Xue and Xuanqin Mou and Lei Zhang",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; 1st Chinese Conference on Computer Vision, CCCV 2015 ; Conference date: 18-09-2015 Through 20-09-2015",
year = "2015",
doi = "10.1007/978-3-662-48558-3\_42",
language = "英语",
isbn = "9783662485576",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "418--428",
editor = "Xilin Chen and Hongbin Zha and Qiguang Miao and Liang Wang",
booktitle = "Computer Vision CCF Chinese Conference, CCCV 2015, Proceedings",
}