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.
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Xue, W., Mou, X., Zhang, L. (2015). Decoupled Marginal Distribution of Gradient Magnitude and Laplacian of Gaussian for Texture Classification. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_42
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