Earticle

초록

영어

Automatic image annotation (AIA) is an active topic of research in computer vision and pattern recognition. In the last two decades, large amount of researches on AIA have been proposed, mainly including classification-based methods and probabilistic modeling methods. As one of the most common methods for AIA, support vector machine (SVM) has been widely applied in the multimedia research community, especially for image classification, image annotation and retrieval. However, compared with various SVM methods and their corresponding applications in the literature, there is almost no review research and analysis about SVM related studies. So the current paper, to start with, elaborates the basic principles of SVM. Followed by it summarizes SVM with applications to image annotation from three aspects of SVM ensemble for AIA, SVM with mixture of kernels for AIA and hybrid SVM for AIA respectively. In addition, SVM exploited in several other applications are also briefly reviewed. Finally, we end this paper with a summary of some important conclusions and highlight the potential research directions of SVM in automatic image annotation for the future.

목차

Abstract
 1. Introduction
 2. Support Vector Machine
 3. SVM for Image Annotation
  3.1. SVM Ensemble for AIA
  3.2. SVM with Mixture of Kernels for AIA
  3.3. Hybrid SVM for AIA
 4. SVM for Other Applications
 5. Conclusions
 References

키워드

저자

• Dongping Tian [ Institute of Computer Software, Baoji University of Arts and Sciences, Baoji, Shaanxi, 721007, China, Institute of Computational Information Science, Baoji University of Arts and Sciences, Baoji, Shaanxi, 721007, 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