The Multi-scale Dominant Binary Pattern Learning for Image Recognition

This paper proposes an image representation method based on image local microstructure binary pattern extraction. By means of zero-mean microstructure pattern binarization (ZMPB), the extracted binary microstructure pattern can express all the important patterns with visual meaning that may occur in the image. Moreover, through the dominant binary pattern learning model, we can obtain the dominant feature pattern set adapted to the different data sets, which achieves excellent ability in feature robustness, discriminative and representation. Meanwhile, through dominant binary pattern learning, the dimension of feature coding can be greatly reduced and the execution speed of the algorithm can be improved. In order to verify the effectiveness of the algorithm, experiments are carried out on the public ORL, YALE face data sets, MNIST handwritten digital data set and a non-public car logo data set. The experimental results show that our method has strong discriminative power and outperforms the traditional LBP and GIMMRP methods. Compared with many recent algorithms, our method also presents a competitive advantage.