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.
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