Learning Fine-grained Image Similarity with Deep Ranking
It addresses the challenge of capturing subtle image differences for tasks like image retrieval, representing an incremental improvement over existing methods.
The paper tackles the problem of learning fine-grained image similarity by proposing a deep ranking model that directly learns similarity metrics from images, outperforming hand-crafted feature and deep classification models in experiments.
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.