Image similarity has been extensively studied in computer vision. In recently years, machine-learned models have shown their ability to encode more semantics than traditional multivariate metrics. However, in labelling similarity, assigning a numerical score to a pair of images is less intuitive than determining if an image A is closer to a reference image R than another image B. In this work, we present a novel approach for building an image similarity model based on labelled data in the form of A:R vs B:R. We address the challenges of sparse sampling in the image space (R, A, B) and biases in the models trained with context-based data by using an ensemble model. In particular, we employed two ML techniques to construct such an ensemble model, namely dimensionality reduction and MLP regressors. Our testing results show that the ensemble model constructed performs ~5% better than the best individual context-sensitive models. They also performed better than the model trained with mixed imagery data as well as existing similarity models, e.g., CLIP and DINO. This work demonstrate that context-based labelling and model training can be effective when an appropriate ensemble approach is used to alleviate the limitation due to sparse sampling.

翻译：图像相似度研究在计算机视觉领域已得到广泛探索。近年来，机器学习模型展现出比传统多变量度量方法更强的语义编码能力。然而，在相似度标注过程中，为图像对赋予数值评分并不如判断"图像A相较于另一图像B是否更接近参考图像R"更为直观。本研究提出了一种基于A:R与B:R形式标注数据构建图像相似度模型的新方法。我们通过集成模型解决了图像空间(R, A, B)采样稀疏性及基于上下文数据训练的模型偏差问题。具体而言，我们采用两种机器学习技术构建该集成模型：降维与多层感知器回归器。测试结果表明，所构建的集成模型性能比性能最佳的单一上下文敏感模型提升约5%。此外，该模型性能优于混合图像数据训练的模型以及现有相似度模型（如CLIP与DINO）。本研究证明，当采用适当的集成方法缓解稀疏采样带来的局限性时，基于上下文的标注与模型训练能够取得良好效果。