Learning Fine-Grained Image Similarity with Deep Ranking
Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than m...
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| Vydáno v: | 2014 IEEE Conference on Computer Vision and Pattern Recognition s. 1386 - 1393 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Konferenční příspěvek Journal Article |
| Jazyk: | angličtina |
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01.06.2014
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| ISSN: | 1063-6919, 1063-6919 |
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| Abstract | Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is also proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models. |
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| AbstractList | Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is also proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models. |
| Author | Ying Wu Jiang Wang Yang Song Jingbin Wang Leung, Thomas Rosenberg, Chuck Bo Chen Philbin, James |
| Author_xml | – sequence: 1 surname: Jiang Wang fullname: Jiang Wang email: jwa368@eecs.northwestern.edu – sequence: 2 surname: Yang Song fullname: Yang Song email: yangsong@google.com – sequence: 3 givenname: Thomas surname: Leung fullname: Leung, Thomas email: leungt@google.com – sequence: 4 givenname: Chuck surname: Rosenberg fullname: Rosenberg, Chuck email: chuck@google.com – sequence: 5 surname: Jingbin Wang fullname: Jingbin Wang email: jingbinw@google.com – sequence: 6 givenname: James surname: Philbin fullname: Philbin, James email: jphilbin@google.com – sequence: 7 surname: Bo Chen fullname: Bo Chen email: bchen3@caltech.edu – sequence: 8 surname: Ying Wu fullname: Ying Wu email: yingwu@eecs.northwestern.edu |
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| Snippet | Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep... |
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| SubjectTerms | Algorithms Computational modeling Computer architecture Computer vision Conferences Learning Load modeling Mathematical models Neural networks Pattern recognition Ranking Semantics Similarity Training data Visualization |
| Title | Learning Fine-Grained Image Similarity with Deep Ranking |
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