Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Framework of High Dimensional Image Classification with Kernel PCA and SVM with Hyper-parameter Optimization
 3. Feature Extraction with PHOW
 4. Descriptor Transformation with Additive Kernel
 5. Descriptors Dimensionality Reduction by KPCA
  5.1. Linear PCA
  5.2. Kernel Principal Component Analysis
 6. Model Training and Classifying by SVM with Randomized Hyper-Parameter Optimization
  6.1. Support Vector Machine
  6.2. Hyper-parameter optimization
 7. Experiments
  7.1. Experiments and Discussions
 8. Conclusions
 Acknowledgements
 References

키워드

저자

• Lin Li [ University of Electronic Science and Technology of China, Chengdu 611731, China, Sichuan TOP IT Vocational Institute ]
• Jin Lian [ Sichuan TOP IT Vocational Institute ]
• Yue Wu [ University of Electronic Science and Technology of China, Chengdu 611731, China ]
• Mao Ye [ University of Electronic Science and Technology of China, Chengdu 611731, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605