In this paper, we present a novel machine learning-based image ranking approach using Convolutional Neural Networks (CNN). Our proposed method relies on a similarity metric learning algorithm operating on lists of image examples and a loss function taking into account the ranking in these lists with respect to different query images. This comprises two major contributions: (1) Rank lists instead of image pairs or triplets are used for training, thus integrating more explicitly the order of similarity and relations between sets of images. (2) A weighting is introduced in the loss function based on two evaluation measures: the mean average precision and the rank 1 score. We evaluated our approach on two different computer vision applications that are commonly formulated as ranking problems: person re-identification and image retrieval with several public benchmarks and showed that our new loss function outperforms other common functions and that our method achieves state-of-the-art performance compared to existing approaches from the literature.

在本文中，我们提出了一种使用卷积神经网络（CNN）的新颖的基于机器学习的图像排名方法。我们提出的方法依赖于在图像示例列表上运行的相似性度量学习算法和损失函数，其中考虑了这些列表中相对于不同查询图像的排名。这包括两个主要贡献：（1）使用等级列表而不是图像对或三元组进行训练，从而更明确地整合图像组之间相似性和关系的顺序。（2）基于两种评估方法在损失函数中引入权重：平均平均精度和等级1得分。我们在通常被定义为排名问题的两个不同的计算机视觉应用程序上评估了我们的方法：