Image Classification Based on KPCA and SVM with Randomized Hyper-parameter Optimization

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 produce the image feature representation by extracting pyramid histogram of visual word (PHOW) descriptors of image, then map the descriptors though additive kernels. At the third step we use KPCA for feature dimensionality reduction. Finally we classify image by SVM with randomized hyper-parameter optimization. Extensive experiments are tested on two data sets: Msrcv2, 15-Scenes. These experiments justify that (1) feature descriptors with KPCA is superior to that with PCA for dimensionality reduction;(2)SVM with randomized hyper-parameter optimization greatly saves time while keeping high accuracy.