.png){kind=icon}
返回
Multimedia automatic annotation by mining label set correlation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-017-5170-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Organizing and retrieving multimedia data heavily rely on the relevant textual descriptions. Multimedia automatic annotation, which assigns text labels to multimedia samples, has been widely studied. Among others, search-based annotation methods are well suited for annotation tasks on large-scale datasets and are studied in depth because of their simplicity and scalability. However, classical search based annotation methods address this problem by treating each label independently, which ignores the correlation between different labels in the assigned label set. This paper aims to integrate the relevant information of the label set with respect to the multimedia content and the inner correlated information of the label set into a joint learning framework. We evaluate the performance of the proposed method on MIRFLICKR-25000 and NUS-WIDE datasets. Experimental results show that the proposed annotation method achieves excellent performance.
Keyword:
Multimedia annotation
Automatic annotation
Label set correlation mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Contribution of left supramarginal and angular gyri to episodic memory encoding: An intracranial EEG study.png)
NeuroImage
OAAI
NeuroImageIF0
OAAI分享收藏
查看更多内容