系统仿真学报 ›› 2016, Vol. 28 ›› Issue (11): 2860-2867.doi: 10.16182/j.issn1004731x.joss.201611029

• 仿真应用工程 • 上一篇    下一篇

基于上下文语境传播的多媒体语义标注改善

田枫, 尚福华   

  1. 东北石油大学计算机与信息技术学院,大庆 163318;
  • 收稿日期:2016-04-30 修回日期:2016-07-14 出版日期:2016-11-08 发布日期:2020-08-13
  • 作者简介:田枫(1980-), 男, 黑龙江安达, 博士, 副教授, 研究方向为多媒体理解; 尚福华(通讯作者1962-), 男, 吉林延吉, 教授, 博士, 研究方向为机器学习。
  • 基金资助:
    国家自然科学基金(61502094, 61402099); 黑龙江省自然科学基金(F2016002, F2015020); 黑龙江省教育科学规划重点课题(GJB1215019)

Multimedia Annotation Refinement Based on Contextual Information Diffusion

Tian Feng, Shang Fuhua   

  1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
  • Received:2016-04-30 Revised:2016-07-14 Online:2016-11-08 Published:2020-08-13

摘要: 提出了一种数据驱动的多媒体对象标注改善方法,利用数据集蕴含的丰富语境相关信息对基本多媒体对象标注结果进行优化。以标签为节点,相关度为边,构造标签语境相关图,实现概念空间上的相关性传播;将多媒体对象内容特征和文本模态特征互增强过程集成为一个优化框架,通过近似求解策略,实现上下文语境信息传播。该方法充分利用了标签的上下文相关性和多媒体对象的内容相关性。数据集上的实验结果表明,该方法可大幅度提升标注性能。

关键词: 多媒体标注, 语义标注, 语义改善, 语境传播

Abstract: A data driven multimedia annotation refinement method based on dataset contextual information diffusion was proposed. The label contextual graph was constructed, and the label correlation can be diffused on textual label space; Multimedia object content relevant graph was constructed. Label contextual graph and multimedia object content relevant graph were mutually reinforced and formulated into a regularized framework. The proposed method incorporated both multimedia content correlation and label contextual information, and the optimization process was solved by approximate solution algorithm. The experimental results on real world dataset show that the proposed method can obviously improve the annotation performance.

Key words: multimedia annotation, semantic annotation, annotation refinement, contextual diffusion

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