Geometric invariants and object recognition
International Journal of Computer Vision
Texture Classification Using Windowed Fourier Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Projective Invariant for Textures
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Local, semi-local and global models for texture, object and scene recognition
Local, semi-local and global models for texture, object and scene recognition
Texture databases - A comprehensive survey
Pattern Recognition Letters
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A main challenge for texture analysis is to construct a compact texture descriptor which is not only highly discriminative to intra-class textures, but also robust to inter-class variations, geometric and photometric changes. In this paper, a new texture descriptor is developed by integrating the local affine-invariant texture features and the global viewpoint-invariant statistics. Based on the pixel clustering using two state-ofart robust local texture descriptors (i.e. SIFT and SPIN), the proposed texture descriptor enables impressive invariance to a wide range of environmental changes (e.g. view changes, illumination changes, surface distortions) by characterizing the spatial distribution of pixel sets using multi-fractal analysis. Experiments on some real datasets (publicly available) showed that the proposed texture descriptor achieved better performance than some state-of-art techniques in texture retrieval and texture classification while the computation cost is significantly reduced.