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Abstract：
A dense micro-block difference (DMD)-based method was proposed for performing texture representation that is a fundamental task of image and video analysis. However, it cannot capture effectively the rotation invariance and multiscale spatial information of textures. To alleviate these problems, in this paper, we propose a multiscale symmetric DMD (MSDMD) method for texture classification. In particular, we first combine K-rotation and Gaussian distribution to analyze the Symmetric DMD in order to capture the rotation invariance of textures. Furthermore, we propose a high-order vector of locally aggregated descriptor called HVLAD by incorporating the second-order and third-order statistics into the original vector of VLAD. To effectively extract the spatial information of textures, we implement the above-mentioned steps in a Gaussian pyramid structure to construct an MSDMD feature and use a support vector machine (SVM) to perform texture classification. The experimental results on five available published texture datasets (KTH-TIPS, CUReT, UIUC, UMD, and KTH-TIPS2-b) reveal that our proposed method is effective when compared with 15 representative texture classification methods. © 1991-2012 IEEE.
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IEEE Transactions on Circuits and Systems for Video Technology
ISSN： 1051-8215
Year： 2019
Issue： 12
Volume： 29
Page： 3583-3594
4 . 1 3 3
JCR@2019
8 . 3 0 0
JCR@2023
ESI HC Threshold：150
CAS Journal Grade：2
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