Title :
Texture classification with kernel principal component analysis
Author :
Kim, Kwang In ; Jung, K. ; Park, S.H. ; Kim, H.J.
Author_Institution :
Dept. of Comput. Eng., Kyungpook Nat. Univ., Taegu, South Korea
fDate :
6/8/2000 12:00:00 AM
Abstract :
Kernel principal component analysis (PCA) is presented as a mechanism for extracting textural information. Using the polynomial kernel, higher order correlations of input pixels can be easily used as features for classification. As a result, supervised texture classification can be performed using a neural network
Keywords :
correlation theory; feature extraction; image classification; image texture; neural nets; principal component analysis; higher order correlations; input pixels; kernel principal component analysis; neural network; polynomial kernel; supervised texture classification; textural information;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20000780
