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Multi-View 3D Object Retrieval With Deep Embedding Network

文献类型：期刊论文

作者Guo, Haiyun1,2; Wang, Jinqiao1,2; Gao, Yue3; Li, Jianqiang4; Lu, Hanqing1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2016-12-01
卷号25期号:12页码:5526-5537
关键词Convolutional Neural Network Multi-view 3d Object Retrieval Triplet Loss
DOI10.1109/TIP.2016.2609814
文献子类Article
英文摘要In multi-view 3D object retrieval, each object is characterized by a group of 2D images captured from different views. Rather than using hand-crafted features, in this paper, we take advantage of the strong discriminative power of convolutional neural network to learn an effective 3D object representation tailored for this retrieval task. Specifically, we propose a deep embedding network jointly supervised by classification loss and triplet loss to map the high-dimensional image space into a low-dimensional feature space, where the Euclidean distance of features directly corresponds to the semantic similarity of images. By effectively reducing the intra-class variations while increasing the inter-class ones of the input images, the network guarantees that similar images are closer than dissimilar ones in the learned feature space. Besides, we investigate the effectiveness of deep features extracted from different layers of the embedding network extensively and find that an efficient 3D object representation should be a tradeoff between global semantic information and discriminative local characteristics. Then, with the set of deep features extracted from different views, we can generate a comprehensive description for each 3D object and formulate the multi-view 3D object retrieval as a set-to-set matching problem. Extensive experiments on SHREC'15 data set demonstrate the superiority of our proposed method over the previous state-of-the-art approaches with over 12% performance improvement.
WOS关键词MODEL RETRIEVAL ; VISUAL SIMILARITY ; DISTANCE ; DESCRIPTOR
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000388205100002
资助机构863 Program(2014AA015104) ; National Natural Science Foundation of China(61273034 ; 61332016)
源URL[http://ir.ia.ac.cn/handle/173211/13354]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Tsinghua Univ, Sch Software, Tsinghua Natl Lab Informat Sci & Technol TNList, Key Lab Informat Syst Secur,Minist Educ, Beijing 100084, Peoples R China
4.Beijing Univ Technol, Sch Software Engn, Beijing Engn Res Ctr IoT Software & Syst, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Haiyun,Wang, Jinqiao,Gao, Yue,et al. Multi-View 3D Object Retrieval With Deep Embedding Network[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(12):5526-5537.
APA Guo, Haiyun,Wang, Jinqiao,Gao, Yue,Li, Jianqiang,&Lu, Hanqing.(2016).Multi-View 3D Object Retrieval With Deep Embedding Network.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(12),5526-5537.
MLA Guo, Haiyun,et al."Multi-View 3D Object Retrieval With Deep Embedding Network".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.12(2016):5526-5537.

入库方式： OAI收割

来源：自动化研究所

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