Learning Non-Metric Visual Similarity for Image Retrieval

Noa Garcia, George Vogiatzis

Feb 15, 2018 (modified: Oct 26, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: 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.
  • TL;DR: Similarity network to learn a non-metric visual similarity estimation between a pair of images
  • Keywords: image retrieval, visual similarity, non-metric learning
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