CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Deep neural network based image annotation
Zhu, Songhao1; Shi, Zhe1; Sun, Chengjian1; Shen, Shuhan2
AbstractMultilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two stage learning scheme that consists of (i) learning to fine-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on a variety of public datasets evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms. (C) 2015 Elsevier B.V. All rights reserved.
KeywordDeep Learning Multi-label Multi-modal Image Annotation
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000362187000015
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Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210046, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 110093, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Songhao,Shi, Zhe,Sun, Chengjian,et al. Deep neural network based image annotation[J]. PATTERN RECOGNITION LETTERS,2015,65:103-108.
APA Zhu, Songhao,Shi, Zhe,Sun, Chengjian,&Shen, Shuhan.(2015).Deep neural network based image annotation.PATTERN RECOGNITION LETTERS,65,103-108.
MLA Zhu, Songhao,et al."Deep neural network based image annotation".PATTERN RECOGNITION LETTERS 65(2015):103-108.
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