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Title: Integrating local feature and global statistics for texture analysis
Authors: Xu, Y.
Huang, S.
Ji, H. 
Keywords: Feature extraction
Image classification
Image recognition
Image texture analysis
Pattern recognition
Issue Date: 2009
Source: Xu, Y., Huang, S., Ji, H. (2009). Integrating local feature and global statistics for texture analysis. Proceedings - International Conference on Image Processing, ICIP : 1377-1380. ScholarBank@NUS Repository.
Abstract: 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-of-art 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. ©2009 IEEE.
Source Title: Proceedings - International Conference on Image Processing, ICIP
ISBN: 9781424456543
ISSN: 15224880
DOI: 10.1109/ICIP.2009.5413361
Appears in Collections:Staff Publications

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