| Citation: | Dongbo ZHANG, Liangling YI, Haixia XU, Ying ZHANG. Multi-scale Local Region Structure Dominant Binary Pattern Learning for Image Representation[J]. Journal of Electronics & Information Technology, 2019, 41(4): 896-903. doi: 10.11999/JEIT180512
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By means of Zero-mean Microstructure Pattern Binarization (ZMPB), an image representation method based on image local microstructure binary pattern extraction is proposed. The method can express all the important patterns with visual meaning that may occur in the image. Moreover, through the dominant binary pattern learning model, the dominant feature pattern set adapted to the different data sets is obtained, which not noly achieves excellent ability in feature robustness, discriminative and representation, but also can greatly reduce the dimension of feature coding and improve the execution speed of the algorithm. The experimental results show that the proposed method has strong discriminative power and outperformes the traditional LBP and GIMMRP methods. Compared with many recent algorithms, the proposed method also presents a competitive advantage.
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