CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Large Scale Image Annotation via Deep Representation Learning and Tag Embedding Learning
He,Yonghao; Wang,Jian; Kang,Cuicui; Xiang,Shiming; Pan,Chunhong
Conference NameACM International Conference on Multimedia Retrieval
Source PublicationProceedings of the 5th ACM on International Conference on Multimedia Retrieval
Conference Date2015-6-23
Conference PlaceShanghai, China
AbstractIn 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.

KeywordLarge Scale Image Annotation Deep Representation Learning Tag Embedding Learning
Indexed ByEI
Document Type会议论文
Corresponding AuthorHe,Yonghao
AffiliationNLPR, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
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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