Knowledge Commons of Institute of Automation,CAS
Multi-View 3D Object Retrieval With Deep Embedding Network | |
Guo, Haiyun1,2; Wang, Jinqiao1,2; Gao, Yue3; Li, Jianqiang4; Lu, Hanqing1,2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
2016-12 | |
卷号 | 25期号:12页码:5526-5537 |
文章类型 | Article |
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. |
关键词 | Convolutional Neural Network Multi-view 3d Object Retrieval Triplet Loss |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2016.2609814 |
关键词[WOS] | MODEL RETRIEVAL ; VISUAL SIMILARITY ; DISTANCE ; DESCRIPTOR |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | 863 Program(2014AA015104) ; National Natural Science Foundation of China(61273034 ; 61332016) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000388205100002 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/13354 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
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 |
第一作者单位 | 模式识别国家重点实验室 |
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. |
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