Earticle

초록

영어

In this paper, we present a new method for automatic image annotation by applying semi- supervised learning based on the Bayesian framework. On the one hand, we employ the semi- supervised learning, i.e., transductive support vector machine (TSVM) to enhance the quality of training image data, which is a promising way to find out the underlying relevant data from the unlabeled ones. On the other hand, a simple yet very efficient Bayesian model is built to implement image annotation by the maximum a posteriori (MAP) criterion. The novelty of our method mainly lies in two aspects: exploiting TSVM to improve the quality of training image dataset and utilizing the Bayesian model to predict the candidate annotations for the unseen images. Experimental results on the standard Corel dataset demonstrate that the proposed method is superior or highly competitive to several state-of-the-art approaches.

목차

Abstract
 1. Introduction
 2. Semi-Supervised Learning
 3. Bayesian Framework for Automatic Image Annotation
 4. Experimental Results and Analysis
 5. Conclusions
 Acknowledgements
 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 Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605