Measuring visual (dis)similarity between two or more instances within a data distribution is a fundamental task in many applications, specially in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the similarity model. In this work, we analyze a simple approach for deep learning networks to be used as an approximation of non-metric similarity functions and we study how these models generalize across different image retrieval datasets.
Similarity network to learn a non-metric visual similarity estimation between a pair of images The authors propose learning similarity measure for visual similarity and obtain by this an improvement in very well-known datasets of Oxford and Paris for image retrival. The paper argues that it is more suitable to use non-metric distances instead of metric distances.