Shape Classification Using Multiple Classifiers with Different Feature Sets

Abstract:

Article Preview

In this paper, a new shape classification method based on different feature sets using multiple classifiers is proposed. Different feature sets are derived from the shapes by using different extraction methods. The implements of feature extraction are based on two ways: Fourier descriptors and Zernike moments. Multiple classifiers comprise Normal densities based linear classifier, k-nearest neighbor classifier, Feed-Forward neural network, Radial Basis Function neural network classifier. Each classifier is trained by two feature sets respectively to form two classification results. The final classification results are a combined response of the individual classifier using six different classifier combination rules and the results were compared with those derived from multiple classifiers based on the same feature sets and individual classifier. In this study we examined the different classification tasks on Kimia dataset. For the tasks the best combination strategy was found using the product rule, giving an average recognition rate of 95.83%.

Info:

Periodical:

Advanced Materials Research (Volumes 368-373)

Edited by:

Qing Yang, Li Hua Zhu, Jing Jing He, Zeng Feng Yan and Rui Ren

Pages:

1583-1587

DOI:

10.4028/www.scientific.net/AMR.368-373.1583

Citation:

J. Y. Chen et al., "Shape Classification Using Multiple Classifiers with Different Feature Sets", Advanced Materials Research, Vols. 368-373, pp. 1583-1587, 2012

Online since:

October 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.