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Image Semantic Distance Metric Learning Approach for Large-scale Automatic Image Annotation

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成果类型:
会议论文
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:
Large-scale Automatic Image Annotation;Image Semantic Distance Metric Learning;Improve Performance;Semantic Similarity
期刊:
IOTBD: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND BIG DATA
年:
2016
页码:
277-283
会议名称:
International Conference on Internet of Things and Big Data (IoTBD)
会议时间:
APR 23-25, 2016
会议地点:
Rome, ITALY
会议主办单位:
[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.
主编:
Ramachandran, M Wills, G Walters, R Munoz, VM Chang, V
出版地:
AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL
出版者:
SCITEPRESS
ISBN:
978-989-758-183-0
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:
Learning an effective semantic distance measure is very important for the practical application of image analysis and pattern recognition. Automatic image annotation (AIA) is a task of assigning one or more semantic concepts to a given image and a promising way to achieve more effective image retrieval and analysis. Due to the semantic gap between low-level visual features and high-level image semantic, the performances of some image distance metric learning (IDML) algorithms only using low-level visual features is not satisfactory. Since there is the diversity and complexity of large-scale im...

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