Multi-label Supervised Manifold Ranking for Multi-instance Image Retrieval

被引：0

作者：

Zeng, Xianhua ^{[1
]}

Lv, Renjie

Lian, Hao

机构：

[1] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China

来源：

ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014 | 2014年 / 8818卷

基金：

中国国家自然科学基金;

关键词：

Manifold ranking; Multi-label learning; Multi-instance learning; Image retrieval;

D O I：

10.1007/978-3-319-11740-9_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Current manifold ranking is mainly used in single-instance image retrieval without considering the prevailing semantic ambiguity problem. This paper introduces multi-instance technique and supervised information to image retrieval based on manifold ranking, and proposes a Multi-label Supervised Manifold Ranking algorithm (MSMR) for multi-instance image retrieval. The divergence between images is modified by using the multi-label information of training samples. Our method can solve partly the 'input ambiguity problem' in the feature extraction stage and the 'output ambiguity problem' in the output stage. Compared with the traditional Expectation Maximization Diverse Density (EMDD) and Citation-kNN algorithm on Corel Image Set, the multi-instance image retrieval experimental results show that the average precision rate of our algorithm has be enhanced

引用

页码：423 / 431

页数：9