Earticle

초록

영어

Automatic image annotation has been an active research topic in recent years due to its potential impact on both image understanding and semantic based image retrieval. However, the results of the state-of-the-art image annotation methods are still far from satisfaction due to the existence of semantic gap. Thus refining image annotation (RIA) has become one of the core research topics in computer vision and multimedia areas, whose purpose is to reserve the highly correlated annotations whereas remove the irrelevant or weakly relevant annotations by fully exploring the correlations of annotation keywords. RIA, to some extent, can effectively mitigate the semantic gap between low-level visual features and high-level semantic concepts. So in this paper, we focus on the latest development in image retrieval and provide a comprehensive survey on refining image annotation techniques. In particular, we analyze the key aspects of various RIA methods, including their original intentions and annotation models. Finally, we draw some important conclusions and highlight the potential research directions for the future.

목차

Abstract
 1. Introduction
 2. Refining Image Annotation Techniques
  2.1. Graphical model based RIA
  2.2. Random field model based RIA
  2.3. Manifold Ranking based RIA
  2.4. Other Refining Approaches
 3. Discussion and Conclusions
 Acknowledgements
 References

키워드

저자

• Dong ping Tian [ Institute of Computer Software, Baoji University of Arts and Sciences, 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 Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605