Binarization Representation of Image Microstructure and the Application of Object Recognition

ZHANG Dongbo; CHEN Zhiqiang; YI Liangling; XU Haixia

doi:10.11999/JEIT170513

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 633-640

ZHANG Dongbo, CHEN Zhiqiang, YI Liangling, XU Haixia. Binarization Representation of Image Microstructure and the Application of Object Recognition[J]. Journal of Electronics & Information Technology, 2018, 40(3): 633-640. doi: 10.11999/JEIT170513

Citation:

ZHANG Dongbo, CHEN Zhiqiang, YI Liangling, XU Haixia. Binarization Representation of Image Microstructure and the Application of Object Recognition[J]. Journal of Electronics & Information Technology, 2018, 40(3): 633-640. doi: 10.11999/JEIT170513

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Binarization Representation of Image Microstructure and the Application of Object Recognition

doi: 10.11999/JEIT170513

Funds:

The National Natural Science Foundation of China (61602397), The Natural Science Foundation of Hunan Province (2017JJ2251, 2017JJ3315), The Key Discipline Construction Project of Hunan Province

Received Date: 2017-05-27
Rev Recd Date: 2017-10-19
Publish Date: 2018-03-19

Abstract

Abstract

A novel expression based on Binary Image Microstructure Pattern (BIMP) and Gray Image Micorstructure Maximum Response Pattern (GIMMRP) coding method is proposed. Through the binary coding of the 33 neighborhood structure of the image, the description of the microstructure of the image is obtained, and then selecting the important execution mode subset and the pooling operation to realize the representation of the whole image. In order to verify the effectiveness of the algorithm, experiments are carried out on the ORL, YALE two human face data set, MNIST, USPS two handwritten digital public data sets, as well as non-public vehicle standard data set. The results show the method has strong discriminative power and robustness and can achieve better performance than many of the latest algorithms.

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