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
Hlavní autoři: Jiang Wang, Yang Song, Leung, Thomas, Rosenberg, Chuck, Jingbin Wang, Philbin, James, Bo Chen, Ying Wu
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.06.2014
Témata:
Algorithms
Computational modeling
Computer architecture
Computer vision
Conferences
Learning
Load modeling
Mathematical models
Neural networks
Pattern recognition
Ranking
Semantics
Similarity
Training data
Visualization
ISSN:1063-6919, 1063-6919
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Shrnutí: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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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.180