Learning Fine-Grained Image Similarity with Deep Ranking

Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, Ying Wu

CVPR 2014

doi:10.1109/CVPR.2014.180 /cvpr/2014/wang2014cvpr-learning/

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.

PDF CVPR Semantic Scholar

Cite

Text

Wang et al. "Learning Fine-Grained Image Similarity with Deep Ranking." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.180

Markdown

[Wang et al. "Learning Fine-Grained Image Similarity with Deep Ranking." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/wang2014cvpr-learning/) doi:10.1109/CVPR.2014.180

BibTeX

@inproceedings{wang2014cvpr-learning,
  title     = {{Learning Fine-Grained Image Similarity with Deep Ranking}},
  author    = {Wang, Jiang and Song, Yang and Leung, Thomas and Rosenberg, Chuck and Wang, Jingbin and Philbin, James and Chen, Bo and Wu, Ying},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.180},
  url       = {https://mlanthology.org/cvpr/2014/wang2014cvpr-learning/}
}