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Image distance metric learning based on neighborhood sets for automatic image annotation

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成果类型:
期刊论文
作者:
Jin, Cong*;Jin, Shu-Wei
通讯作者:
Jin, Cong
作者机构:
[Jin, Cong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
[Jin, Shu-Wei] Ecole Normale Super, Dept Phys, 24 Rue Lhomond, F-75231 Paris 5, France.
通讯机构:
[Jin, Cong] C
Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Automatic image annotation;Improve performance;Image distance metric learning;Neighborhood sets;Algorithm performance;Visual similarity;Semantic similarity;Probability density ratio
期刊:
Journal of Visual Communication and Image Representation
ISSN:
1047-3203
年:
2016
卷:
34
期:
C
页码:
167-175
基金类别:
Natural Social Science Foundation of China [13BTQ050]
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Since there is semantic gap between low-level visual features and high-level image semantic, the performance of many existing content-based image annotation algorithms is not satisfactory. In order to bridge the gap and improve the image annotation performance, a novel automatic image annotation (AIA) approach using neighborhood set (NS) based on image distance metric learning (IDML) algorithm is proposed in this paper. According to IDML, we can easily obtain the neighborhood set of each image since obtained image distance can effectively measure the distance between images for AIA task. By in...

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