[Paper] Visual Instance Retrieval with Deep Convolutional Networks
This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local featu...
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| Published in | ITE TRANSACTIONS ON MEDIA TECHNOLOGY AND APPLICATIONS Vol. 4; no. 3; pp. 251 - 258 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately. |
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| AbstractList | This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately. |
| Author | Razavian, Ali S. Sullivan, Josephine Carlsson, Stefan Maki, Atsuto |
| Author_xml | – sequence: 1 givenname: Ali S. surname: Razavian fullname: Razavian, Ali S. organization: Royal Institute of Technology (KTH) – sequence: 2 givenname: Josephine surname: Sullivan fullname: Sullivan, Josephine organization: Royal Institute of Technology (KTH) – sequence: 3 givenname: Stefan surname: Carlsson fullname: Carlsson, Stefan organization: Royal Institute of Technology (KTH) – sequence: 4 givenname: Atsuto surname: Maki fullname: Maki, Atsuto organization: Royal Institute of Technology (KTH) |
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| Cites_doi | 10.1109/CVPR.2007.383266 10.1109/ICCV.2013.177 10.1109/CVPRW.2014.131 10.1109/CVPR.2008.4587635 10.1109/ICCV.2011.6126265 10.1145/2647868.2654948 10.1109/CVPR.2009.5206848 10.1109/TMM.2014.2329648 10.1109/CVPR.2014.222 10.1007/978-3-319-10590-1_53 10.1109/CVPRW.2009.5206529 10.1007/978-3-319-10584-0_26 10.1109/TPAMI.2015.2500224 10.1007/978-3-319-10590-1_38 10.1109/CVPR.2014.269 10.1109/TPAMI.2005.188 10.1109/ICCV.2003.1238663 10.1109/CVPR.2008.4587633 10.1109/CVPR.2007.383172 |
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| SubjectTerms | Convolutional network Learning representation Multi-resolution search Visual instance retrieval |
| Title | [Paper] Visual Instance Retrieval with Deep Convolutional Networks |
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