A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Image Classification Based on KPCA and SVM with Randomized Hyper-parameter Optimization
2014
International Journal of Signal Processing, Image Processing and Pattern Recognition
Image classification is one of the most fundamental and useful activities in computer vision domain. For better accuracy and executing efficiency under the circumstance of high dimensional feature descriptors in image classification, we proposes a novel framework for multi-class image classification based on kernel principal component analysis(KPCA) for feature descriptors post-processing and support vector machine (SVM) with randomized hyper-parameter optimization for classification. We
doi:10.14257/ijsip.2014.7.4.29
fatcat:qwhjvuot7vetpdhr5i4t3mwbee