Large Scale Image Annotation via Deep Representation Learning and Tag Embedding Learning

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Large Scale Image Annotation via Deep Representation Learning and Tag Embedding Learning
	He,Yonghao; Wang,Jian; Kang,Cuicui; Xiang,Shiming; Pan,Chunhong
	2015-06
会议名称	ACM International Conference on Multimedia Retrieval
会议录名称	Proceedings of the 5th ACM on International Conference on Multimedia Retrieval
会议日期	2015-6-23
会议地点	Shanghai, China
摘要	In this paper, we focus on the issue of large scale image annotation, whereas most existing methods are devised for small datasets. A novel model based on deep representation learning and tag embedding learning is proposed. Specifically, the proposed model learns an unified latent space for image visual features and tag embeddings simultaneously. Furthermore, a metric matrix is introduced to estimate the relevance scores between images and tags. Finally, an objective function modeling triplet relationships (irrelevant tag, image, relevant tag) is proposed with maximum margin pursuit. The proposed model is easy to tackle new images and tags via online learning and has a relatively low test computation complexity. Experimental results on NUS-WIDE dataset demonstrate the effectiveness of the proposed model.
关键词	Large Scale Image Annotation Deep Representation Learning Tag Embedding Learning
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11654
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
通讯作者	He,Yonghao
作者单位	NLPR, Institute of Automation, Chinese Academy of Sciences
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	He,Yonghao,Wang,Jian,Kang,Cuicui,et al. Large Scale Image Annotation via Deep Representation Learning and Tag Embedding Learning[C],2015.

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