{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:46:50Z","timestamp":1780552010603,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T00:00:00Z","timestamp":1561420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2016YFC0801800"],"award-info":[{"award-number":["2016YFC0801800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Automatic coal-rock recognition is one of the critical technologies for intelligent coal mining and processing. Most existing coal-rock recognition methods have some defects, such as unsatisfactory performance and low robustness. To solve these problems, and taking distinctive visual features of coal and rock into consideration, the multi-scale feature fusion coal-rock recognition (MFFCRR) model based on a multi-scale Completed Local Binary Pattern (CLBP) and a Convolution Neural Network (CNN) is proposed in this paper. Firstly, the multi-scale CLBP features are extracted from coal-rock image samples in the Texture Feature Extraction (TFE) sub-model, which represents texture information of the coal-rock image. Secondly, the high-level deep features are extracted from coal-rock image samples in the Deep Feature Extraction (DFE) sub-model, which represents macroscopic information of the coal-rock image. The texture information and macroscopic information are acquired based on information theory. Thirdly, the multi-scale feature vector is generated by fusing the multi-scale CLBP feature vector and deep feature vector. Finally, multi-scale feature vectors are input to the nearest neighbor classifier with the chi-square distance to realize coal-rock recognition. Experimental results show the coal-rock image recognition accuracy of the proposed MFFCRR model reaches 97.9167%, which increased by 2%\u20133% compared with state-of-the-art coal-rock recognition methods.<\/jats:p>","DOI":"10.3390\/e21060622","type":"journal-article","created":{"date-parts":[[2019,6,25]],"date-time":"2019-06-25T10:52:31Z","timestamp":1561459951000},"page":"622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Multi-Scale Feature Fusion for Coal-Rock Recognition Based on Completed Local Binary Pattern and Convolution Neural Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Xiaoyang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Geoscience &amp; Surveying Engineering, China University of Mining &amp; Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5035-223X","authenticated-orcid":false,"given":"Yuxing","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Equipment Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.coal.2011.02.003","article-title":"Geochemistry of trace elements in Chinese coals: A review of abundances, genetic types, impacts on human health, and industrial utilization","volume":"94","author":"Dai","year":"2012","journal-title":"Int. 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