Earticle

초록

영어

The traditional recommendation algorithms of image tagging ignore the diversity between the visual content information and the tags recommended, which causes the recommended results have the problem of tag ambiguity, tag redundancy and so on. Therefore, this paper proposes the recommendation algorithm of image tagging based on relevance and diversity. The algorithm defines the relevance and diversity of a label set, and selects a label set which can reasonably balance the relevance and diversity to recommend to the user. The experimental results show that this algorithm improves the relevance between the recommended results and the image, and makes the recommended results be able to reflect the image content thoroughly at the same time.

목차

Abstract
 1. Introduction
 2. Relevance and Visual Distance
  A. The Visual Language Model
  B. Relevance Between The Tag And The Image
  C. The Visual Distance Between The Tags
 3. The Image Tag Recommendation Algorithm
  A. The Relevance and Diversity of the Tag Set
  B. Algorithm Description and Time Complexity
 4. Simulation Test and Analysis
  A. Expeirmental Environment and Settings
  B. The Index Evaluation
 5. Conclusion
 References

키워드

저자

• Guo Zheng-Hong [ School of Information Science and Engineering, Hebei North University, Zhangjiakou 075000, China ]
• Ma Xinhua [ Blood Bank of Zhangjiakou, Zhangjiakou 075000, China ]
• Zhao Kai [ Teaching Affairs Division, Pingdingshan University, Henan Pingdingshan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605