A Recognition Method of Mineral Shape Based on Extreme Learning Machine
Authors
-
Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone, Hunan University of Arts and Science, Changde 415 000 ,CN
Ding Dehong -
Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone, Hunan University of Arts and Science, Changde 415 000 ,CNLi Wuke
-
Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone, Hunan University of Arts and Science, Changde 415 000 ,CNLi Hao
-
Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone, Hunan University of Arts and Science, Changde 415 000 ,CNLi Ling
Keywords:
Mineral Recognition, Mineral Shape, Feature Data Classification, Extreme Learning Machine.Abstract
In view of the situation of the existing algorithm for mineral shape recognition is relatively complex, the individual of strong pertinence and poor robustness, the use of infrared thermal images of minerals multifractal feature data classification recognition method is put forward. Multifractal can describe not only the local details, but also the overall characteristics that has the scale independence and theoretically is suitable for describing the texture characteristics and the distribution of mineral as well as that of energy resource. This paper uses multifractal as parameters of singularity detection of high-dimensional data and learning and understanding of high-dimensional data to distinguish the object/target from infrared heat map. The experimental result show that the infrared thermal image of mineral target in line with the multifractal characteristics, which can be used as one of the effective methods of infrared thermal images detection target. When three kinds of neural network ELM, PNN, GRNN is used for machine learning with obtain fractal parameters, ELM’s accuracy is as high as 84%. While the same training with face natural images is done, ELM is still best, but accuracy is less than 15%. It shows that ELM combining with mineral fractal data has a better performance in classification and pattern recognition.
Downloads
Metrics
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Xu, L., Krzyzak, A. and Suen, C. (1992): “Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition.” IEEE Transactions on Systems, Man and Cybernetics. 1992, 22(3): 418-435.
Brunelli, R. and Falavigna, D. (1995): “Person Identification Using Multiple Cues.” IEEE Transactions on PAMI. 1995, 12: 955-966.
Hong, L. and Jain, Anil K. (1998): “Integrating Faces and Fingerprints for Personal Identification.” IEEE Transactions on PAMI. 1998, 20: 1295-1307.
Kittler, J., Hatef, M., Duin, R. P. and Matas, J. G. (1998): “On Combining Classifiers.” IEEE Transactions on PAMI. 1998, 20: 226-239.
Daugman, John G. (1993): “High Confidence Visual Rconition of Persons by a Test of Statistical Independence.” IEEE Transactions on PAMI. 1993, 15(11):1148-1161.
Ross, Arun and Jain, Anil K. (2003): “Information Fusion in Biometrics.” Pattern Recognition Letters. 2003, 24: 2115-2125.
Ross, A., Jain, Anil K. and Reisman, J. (2003): “A Hybrid Fingerprint Matcher.” Pattern Recognition. 2003, 36: 1661-1673.
Mandelbrot, B. B. and Evertsz, C. J. G. (1990): “The potential distribution around growing fractal clusters.” Nature, 1990, 348(6297):143.
Jian, Yang (2003): “Feature fusion: parallel strategy vs. serial strategy.” Pattern Recognition. 2003, 36(6): 1369-1381.
Cao, Jiawu and Zhu, Qiuyu (2002): .“A Contour-based Image Retrieval Algorithm.” Computer Engineering, 2002, 28(7):159-160.
