Image Classification Recognition for Rock Micro-Thin Section Based on Probabilistic Neural Networks

Abstract:

In order to implement the recognition automation of rock section pore images, a method combined K-means clustering with probabilistic neural network is proposed and applied to rock thin section images. Firstly, K-means clustering is used as segmentation algorithm, the rock images are divided into two types and extracted enough features and it is shown good classification recognition effect on testing dataset. Secondly, 100 pieces of rock image section are used as validation dataset, including 20 groups, each group has 5 images and 200 data samplings. Experiments show that the probabilistic neural network can be used as rock texture classifier, the average correct classification rate is around 95.12%, which can meet the practical application needs.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 2147-2152
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.2147
• Online since: August 2014
• Authors: Xin Shan Wei*, Xiao Hua Qin, Chun Long Rong, Jun Xiang Nan, Guo Jian Cheng
• Keywords: Color Image Segmentation, K-Means Clustering, Pattern Recognition, Probabilistic Neural Network, Rock Thin Section Image

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