Earticle

초록

영어

The image retrieval performance based on single feature is limited. For different kinds of images, it can not a better retrieval result. This paper raises image retrieval method based on weighted multi feature. In each kind of images, each feature precision is the weight evidence. On this basis, we research the existing semantic retrieval technology. Choosing the SVM classification theory which is more mature. Selecting parts of images as training set. Doing the training classification. From the research of different characteristics priority, it raises the image retrieval technology which synthesizes SVM and multi feature. From that, it can get a higher retrieval efficiency.

목차

Abstract
 1. Introduction
 2. Basic Principle of SVM
  2.1 VC Dimension Theory
  2.2 Minimization Principle of Structure Risk
 3. SVM Classification
  3.1 SVM Dichotomies
  3.2 SVM Multi-Classification
 4. Image Retrieval Experiments and Performance Analysis based on SVM Semantic Classification
  4.1 SVM Training Algorithm
  4.2 Experimental Procedure
  4.3 Experimental Results and Analysis
 5. Conclusion
 References

키워드

저자

• Che Chang [ Measuring and Control Technology and Instrumentations,Harbin University of Science and Technology, Harbin, China, School of Engineering,Harbin University, Harbin, China ]
• Yu Xiaoyang [ Measuring and Control Technology and Instrumentations,Harbin University of Science and Technology, Harbin, China ]
• Bai Yamei [ School of Electronic and Information Engineering,Harbin Huade University Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605