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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Bibliographic Details
Published in:2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 1386 - 1393
Main Authors: Jiang Wang, Yang Song, Leung, Thomas, Rosenberg, Chuck, Jingbin Wang, Philbin, James, Bo Chen, Ying Wu
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2014
Subjects:
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
Online Access:Get full text
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Summary: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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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2014.180