基于多尺度几何分析与核匹配追踪的图像识别<sup>*</sup>

Abstract
Figure/Table
References
Related Citation (7)

Download: PDF (847 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract A method for image feature extraction and recognition is proposed. Abundant contour feature information of the image is expressed by Contourlet transform while flatness feature and texture of the image are described by Brushlet transform in the multiscale geometric analysis. The two types of feature information make up feature matrix. And fuzzy Cmean clustering is selected to compress the feature matrix and gain distributed datasets. Then object images are recognized with kernel matching pursuit classifier. The proposed method describes variant image information with different characterization transform then keeps useful original image information. Compared with Contourlet transform and Brushlet transform, the proposed method obtains higher accurate recognition rate with shorter running time.

Key words： Image Recognition Kernel Matching Pursuit Feature Clustering MultiScale Geometric Analysis

Received: 21 May 2007

ZTFLH:

TP751

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	GOU ShuiPing
	JIAO LiCheng

Cite this article:

GOU ShuiPing,JIAO LiCheng. Image Recognition Based on MultiScale Geometric Analysis and Kernel Matching Pursuit[J]. , 2007, 20(6): 776-781.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2007/V20/I6/776

[1] Candès E J. Ridgelets: Theory and Applications. Ph.D Dissertation. Stanford, USA: Stanford University. Department of Statistics, 1998
[2] Meyer F G, Coifman R R. Directional Image Compression with Brushlets // Proc of the IEEESP International Symposium on TimeFrequency and TimeScale Analysis. Paris, France, 1996: 189192
[3] Candès E J, Donoho D L. Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges // Cohen A, Rabut C, Schumaker L L, eds. Curves and Surfaces Fitting. Nashville, USA: Vanderbilt University Press, 1999: 105120
[4] Do M N, Stoeckler V M C J, Welland G V. Beyond Wavelets. Washington, USA: Academic Press, 2002
[5] Jiao Licheng, Tan Shan. Development and Prospect of Image Multiscale Geometric Analysis. Acta Electronica Sinica, 2003, 31(Z1): 19751981 (in Chinese)
(焦李成,谭山.图像的多尺度几何分析:回顾和展望.电子学报, 2003, 31(Z1): 19751981)
[6] Do M N, Vetterli M. Contourlets: A Directional Multiresolution Image Representation // Proc of the 9th IEEE International Conference on Image Processing. Rochester, USA, 2002, Ⅰ: 357360
[7] Meyer F G, Coifman R R. Brushlets: A Tool for Directional Image Analysis and Image Compression. Applied and Computational Harmonic Analysis, 1997, 4(2): 147187
[8] Kosko B. Fuzzy Engineering. Upper Saddle River, USA: Prentice Hall, 1996
[9] Vincent P, Bengio Y. Kernel Matching Pursuit. Machine Learning, 2002, 48(1): 165187
[10] Po D D Y, Do M N. Directional Multiscale Modeling of Images Using the Contourlet Transform. IEEE Trans on Image Processing, 2006, 15(6): 16101620
[11] Li S T, ShaweTaylor J. Texture Classification by Combining Wavelet and Contourlet Features // Proc of the Joint IAPR International Workshop on Structural and Syntactical Pattern Recognition and Statistical Pattern Recognition. Lisbon, Portugal, 2004: 11261134
[12] Tan Shan, Zhang Xiangrong, Jiao Licheng. A BrushletBased Feature Set Applied to Texture Classification. Berlin, Germany: SpringerVerlag, 2004: 11751180
[13] Hu M K. Visual Pattern Recognition by Moment Invariants. IEEE Trans on Information Theory, 1962, 8(2): 179187
[14] Brodatz P. Textures: A Photographic Album for Artists & Designers. New York, USA: Dover, 1966