Digital Image Technology Mineral Feature Extraction Method Research

Jian Peng; Dong Bo Li

doi:10.4028/www.scientific.net/AMR.989-994.3906

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Digital Image Technology Mineral Feature Extraction Method Research
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Digital Image Technology Mineral Feature...

Digital Image Technology Mineral Feature Extraction Method Research

Abstract:

This paper presents a texture classification algorithm using Gabor wavelet and Gray Level Co-occurrence Matrix as feature extraction method and Support Vector Machine as classifier. Gabor transform and Gray Level Co-occurrence Matrix are used to get the features of the digital images, SVM classifiers are followed to build image and realize classification. The results of the experiments have shown that the methods described in this paper can improve the rate of correct classification effectively than traditional method of classification.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3906-3909

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3906

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Online since:

July 2014

Authors:

Jian Peng*, Dong Bo Li

Keywords:

Gabor Wavelet, GLCM, SVM Classifier

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* - Corresponding Author

References

[1] Aksoy S, Haralick RM. Feature normalization and likelihood based similarity measures for image retrieval[J]. PRL, 2001, 22(5): 563-582.

DOI: 10.1016/s0167-8655(00)00112-4

Google Scholar

[2] ZHANG Zhi-an, LIN Li-hua. Image retrieval based on color and spatial features[J]. Journal of Xi'an University of Science and Technology, 2007(3): 35-39.

Google Scholar

[3] GUO Jing, ZHOU Ming-quan, GENG Guo-hua. The fast retrieval algorithm for 3D based on shape[J]. Journal of Xi'an University of Science and Technology, 2007(1): 12-16.

Google Scholar

[4] LIU Wei-cheng, SUN Ji-hong. Image information retrieval based on content[J]. Information Science，2002(4): 18-22.

Google Scholar

[5] SUN Xing-hua. Research on image retrieval based on content[D]. Nanjing: Nanjing University of Science and Technology, (2001).

Google Scholar

[6] Haralick RM, Shanmugam. Texture featuers for image classification[J]. IEEE Trans, 1973, 3(6): 610 -621.

Google Scholar

[7] Mallat S. singularity detection and processing with wavelets[J]. IEEE Trans Info Theory, 1990, 38 (2): 617-643.

DOI: 10.1109/18.119727

Google Scholar

[8] ZHANG Yu-jin, XU Yin, LIU Zhong-wei. By extracting characteristics of image retrieval algorithm test platform[J]. Chinese Journal of Image and Graphics, 2001(6): 44-48.

Google Scholar