Multi-scale local pattern co-occurrence matrix for textural image classification

Sun, Xiangping, Wang, Jin, Chen, Ronghua, She, Mary F. H. and Kong, Lingxue 2012, Multi-scale local pattern co-occurrence matrix for textural image classification, in IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks, IEEE, Piscataway, N. J..

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Title Multi-scale local pattern co-occurrence matrix for textural image classification
Author(s) Sun, Xiangping
Wang, Jin
Chen, Ronghua
She, Mary F. H.
Kong, Lingxue
Conference name International Joint Conference on Neural Networks (2012 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 10-15 Jun. 2012
Title of proceedings IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2012
Conference series International Joint Conference on Neural Networks
Total pages 7
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) LPCM
co-occurrence
local pattern
multi-scale
vector quantization
Summary Textural image classification technologies have been extensively explored and widely applied in many areas. It is advantageous to combine both the occurrence and spatial distribution of local patterns to describe a texture. However, most existing state-of-the-art approaches for textural image classification only employ the occurrence histogram of local patterns to describe textures, without considering their co-occurrence information. And they are usually very time-consuming because of the vector quantization involved. Moreover, those feature extraction paradigms are implemented at a single scale. In this paper we propose a novel multi-scale local pattern co-occurrence matrix (MS_LPCM) descriptor to characterize textural images through four major steps. Firstly, Gaussian filtering pyramid preprocessing is employed to obtain multi-scale images; secondly, a local binary pattern (LBP) operator is applied on each textural image to create a LBP image; thirdly, the gray-level co-occurrence matrix (GLCM) is utilized to extract local pattern co-occurrence matrix (LPCM) from LBP images as the features; finally, all LPCM features from the same textural image at different scales are concatenated as the final feature vectors for classification. The experimental results on three benchmark databases in this study have shown a higher classification accuracy and lower computing cost as compared with other state-of-the-art algorithms.
ISBN 9781467314886
9781467314893
9781467314909
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049555

Document type: Conference Paper
Collection: Institute for Frontier Materials
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