.
Calth101数据库部分图像
部分测试结果如下:
识别率（%）
References:
[1] S. Agarwal, A. Awan, and D. Roth. Learning to Detect Objects in Images via a Sparse, Part-Based Representation.
In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 26, pages 1475–1490, November 2004. [2] S. Belongie, J. Malik, and J. Puzicha. Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(24):509–522, April 2002.
[3] A. Berg, T. Berg, and J. Malik. Shape Matching and Object Recognition using Low Distortion Correspondence. Technical report, U.C. Berkeley, Dec 2004.
[4] S. Boughhorbel, J-P. Tarel, and F. Fleuret. Non-Mercer Kernels for SVM Object Recognition. In British Machine Vision Conference, London, UK, Sept 2004.
[5] C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001.
[6] O. Chapelle, P. Haffner, and V. Vapnik. SVMs for Histogram-Based Image Classification. Transactions on Neural Networks, 10(5), Sept 1999.
[7] T. Cormen, C. Leiserson, and R. Rivest. Intro. to Algorithms. MIT Press, 1990.11
[8] N. Cristianini and J. Taylor. An Introduction to Support Vector Machines and other Kernel-Based Learning Methods.Cambridge University Press, 2000.
[9] J. Eichhorn and O. Chapelle. Object Categorization with SVM: Kernels for Local Features. Technical report, MPI for Biological Cybernetics, 2004.
[10] L. Fei-Fei, R. Fergus, and P. Perona. Learning Generative Visual Models from Few Training Examples: an Incremental Bayesian Approach Tested on 101 Object Cateories. In Workshop on Generative Model Based Vision, Washington,D.C., June 2004.
[11] T. Gartner. A Survery of Kernels for Structured Data. Multi Relational Data Mining, 5(1):49–58, July 2003. [12] K. Grauman and T. Darrell. Fast Contour Matching Using Approximate Earth Mover’s Distance. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition,Washington D.C., June 2004.
[13] E. Hadjidemetriou, M. Grossberg, and S. Nayar. Multiresolution Histograms and their Use for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7):831–847, July 2004. [7]刘忠伟，章毓晋。综合利用颜色和纹理特征的图像检索。通信学报。1999年第5期
[8]王文惠，王展，周良柱，万建伟。基于内容的彩色图像颜色特征的提取方法。计算机辅助设计与图形学学报。2001年第6期
[1]北京现代富博科技有限公司，陈兵旗，孙明。Visual C++实用图像处理专业教程。清华大学出版社。2004年3月。P.132-138
[2]张学工。关于统计学习理论和支持向量机。自动化学报。2000年第6期。P.32-42。 [3]李国正，王猛，曾华军译。支持向量机导论。电子工业出版社。2004年。
[6] 肖靓，顾嗣扬。基于SVM综合利用颜色和纹理特征的图像分类和检索。通信和计算机，2005.