Image classification method using Independent Component Analysis , Neighborhood Averaging and Normalization


The KIPS Transactions:PartB , Vol. 8, No. 4, pp. 389-394, Aug. 2001
10.3745/KIPSTB.2001.8.4.389,   PDF Download:

Abstract

In this paper, we have proposed an image classification method using independent component analysis (ICA), neighborhood averaging and normalization. When we have tried to classify images, the proposed neighborhood averaging and normalization have been used to increase the degree of tolerance. A set of experiments show that the proposed method has increased the degree of noise tolerance compared with principal component analysis (PCA) or ICA without preprocessing.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
J. S. Hong, J. W. Ryu, S. S. Kim, "Image classification method using Independent Component Analysis , Neighborhood Averaging and Normalization," The KIPS Transactions:PartB , vol. 8, no. 4, pp. 389-394, 2001. DOI: 10.3745/KIPSTB.2001.8.4.389.

[ACM Style]
Jun Sik Hong, Jeong Woong Ryu, and Sung Soo Kim. 2001. Image classification method using Independent Component Analysis , Neighborhood Averaging and Normalization. The KIPS Transactions:PartB , 8, 4, (2001), 389-394. DOI: 10.3745/KIPSTB.2001.8.4.389.