Earticle

초록

영어

In this research paper, we conduct theoretical analysis and numerical analysis on novel image classification algorithm based on multi-feature extraction and modified SVM classifier. Image object classification and detection are two important basic problems in the study of computer vision, image segmentation, object tracking, behavior analysis and so on the basis of other high-level vision tasks. Existing image classification method can make full use of every single feature between the complementary characteristics of the extracted features of a large number of redundant information, which can lead to image classification accuracy is not high. For this, put forward an improved support vector machine (SVM) based on characteristics and integrated method of image classification. This method can extract comprehensive description of image content features, using principal component analysis to extract the characteristics of transformation, remove redundant information. The experimental result proves the effectiveness and feasibility of the proposed algorithm. In the final part, we conclude the paper and set up the prospect for the future research.

목차

Abstract
 1. Introduction
 2. The Feature Extraction Techniques and Methodologies
 3. The Proposed Image Classification Methodology
  3.1. The Overview of the SVM and Deep Learning Model
  3.2. The Modelling of the Algorithm
 4. Experiment and Simulation
  4.1. The Experiment Set-up and Initiation
  4.2. The Experimental Result
 5. Conclusion and Summary
 References

키워드

저자

• Bingquan Huo [ Binzhou Polytechnic, Shandong, China ]
• Fengling Yin [ Binzhou Polytechnic, Shandong, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605