Please use this identifier to cite or link to this item:
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
Citation: 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

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 18, 2019


checked on Mar 11, 2019

Page view(s)

checked on Jan 18, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.