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 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.
In REF , a DeepRanking model that integrates the deep learning and ranking model is proposed to learn a fine-grained image similarity ranking model directly from images, rather than from hand-crafted features.
Learning Fine-Grained Image Similarity with Deep Ranking
2014 IEEE Conference on Computer Vision and Pattern Recognition
2014 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science