基于最大池化稀疏編碼的煤巖識別方法

伍云霞; 田一民

doi:10.13374/j.issn2095-9389.2017.07.002

基于最大池化稀疏編碼的煤巖識別方法

doi: 10.13374/j.issn2095-9389.2017.07.002

伍云霞^,,
田一民

中國礦業大學(北京)機電與信息工程學院, 北京 100083

基金項目:

國家自然科學基金重點資助項目(51134024)

國家重點研發計劃資助項目(2016YFC0801800)

詳細信息

中圖分類號: TD672;TP391.41
計量
- 文章訪問數: 901
- HTML全文瀏覽量: 265
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- 被引次數: 0
出版歷程
- 收稿日期: 2017-01-01

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

WU Yun-xia^,,
TIAN Yi-min

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

摘要

摘要: 針對現今煤巖圖像識別方法的缺乏與不足,為了挖掘新的煤巖圖像識別方法以及更好地處理高維煤巖圖像數據,提出了基于最大池化稀疏編碼的煤巖識別方法.本方法在提取煤巖圖像特征時加入了池化操作,在分類識別時采用了集成分類器,即多個弱分類器組成一個強分類器.實驗結果表明:最大池化稀疏編碼的特征提取方式能簡單有效表達煤巖圖像的紋理特征,大大增強煤巖圖像的可區分性,獲得較高的識別率,并且具有良好的識別穩定性.研究結果可為煤巖界面的自動識別提供新的思路和方法.
- 煤巖識別 /
- 圖像處理 /
- 最大池化 /
- 稀疏編碼 /
- 特征提取 /
- 集成分類
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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