Wavelet leaders and bootstrap for multifractal analysis of images
Signal Processing
Viewpoint Invariant Texture Description Using Fractal Analysis
International Journal of Computer Vision
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Shape-based Invariant Texture Indexing
International Journal of Computer Vision
Integrating local feature and global statistics for texture analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Balancing deformability and discriminability for shape matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Texture representations using subspace embeddings
Pattern Recognition Letters
Texture databases - A comprehensive survey
Pattern Recognition Letters
A distinct and compact texture descriptor
Image and Vision Computing
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Image texture analysis has received a lot of attention in the past years. Researchers have developed many texture signatures based on texture measurements, for the purpose of uniquely characterizing the texture. Existing texture signatures, in general, are not invariant to 3D transforms such as view-point changes and non-rigid deformations of the texture surface, which is a serious limitation for many applications. In this paper, we introduce a new texture signature, called the multifractal spectrum (MFS). It provides an efficient framework combining global spatial invariance and local robust measurements. The MFS is invariant under the bi-Lipschitz map, which includes view-point changes and non-rigid deformations of the texture surface, as well as local affine illumination changes. Experiments demonstrate that the MFS captures the essential structure of textures with quite low dimension.