A coal-rock recognition method based on max-pooling sparse coding

WU Yun-xia; TIAN Yi-min

doi:10.13374/j.issn2095-9389.2017.07.002

Volume 39 Issue 7

Jul. 2017

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WU Yun-xia, TIAN Yi-min. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002

Citation:

WU Yun-xia, TIAN Yi-min. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002

WU Yun-xia, TIAN Yi-min. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002

Citation:

WU Yun-xia, TIAN Yi-min. A coal-rock recognition method based on max-pooling sparse coding[J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. doi: 10.13374/j.issn2095-9389.2017.07.002

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A coal-rock recognition method based on max-pooling sparse coding

doi: 10.13374/j.issn2095-9389.2017.07.002

WU Yun-xia^,,
TIAN Yi-min

School of Mechanical Electronic & Information Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China

Received Date: 2017-01-01

Abstract

Abstract

Because of the lack of coal-rock methods, a novel coal-rock recognition method was proposed based on max-pooling sparse coding in order to explore new coal-rock image recognition methods and efficiently handle high-dimensional coal-rock image data. This method adds the pooling operation when extracting coal-rock image features and adopts the integrated classifier, which consists of multiple weak classifiers when classifying coal-rock images. The experimental results show that this feature-extraction method based on max-pooling sparse coding can simply and effectively express the characteristic information of coal-rock images, greatly enhance the distinguishability of coal-rock images, and achieve a high recognition rate. This method also has good recognition stability. The results obtained herein could provide a new idea and method for automatic coal-rock interface recognition.
- coal-rock recognition,
- image processing,
- max-pooling,
- sparse coding,
- feature extraction,
- integrated classification

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References(15)

References

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