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Multi-scale local pattern co-occurrence matrix for textural image classification
conference contribution
posted on 2012-01-01, 00:00 authored by Xiangping Sun, Jin Wang, Ronghua Chen, Fenghua SheFenghua She, Lingxue KongLingxue KongTextural 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.
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Event
International Joint Conference on Neural Networks (2012 : Brisbane, Qld.)Publisher
IEEELocation
Brisbane, Qld.Place of publication
Piscataway, N. J.Start date
2012-06-10End date
2012-06-15ISBN-13
9781467314909ISBN-10
1467314900Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural NetworksUsage metrics
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