Multiscale Symmetric Dense Micro-Block Difference for Texture Classification

• Published in: IEEE transactions on circuits and systems for video technology Vol. 29; no. 12; pp. 3583 - 3594
• Main Authors: Dong, Yongsheng, Wu, Huangbin, Li, Xuelong, Zhou, Chuanqi, Wu, Qingtao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; DMD; Encoding; Feature extraction; Gaussian mixture model; Histograms; Invariance; MSDMD; Multiscale analysis; Normal distribution; Rotation; Spatial data; support vector machine; Support vector machines; Task analysis; Texture; Texture classification; vector of locally aggregated descriptors
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2018.2883825

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.