  The concept of image similarity is ambiguous, and images can be similar in
one context and not in another. This ambiguity motivates the creation of
metrics for specific contexts. This work explores the ability of deep
perceptual similarity (DPS) metrics to adapt to a given context. DPS metrics
use the deep features of neural networks for comparing images. These metrics
have been successful on datasets that leverage the average human perception in
limited settings. But the question remains if they could be adapted to specific
similarity contexts. No single metric can suit all similarity contexts, and
previous rule-based metrics are labor-intensive to rewrite for new contexts. On
the other hand, DPS metrics use neural networks that might be retrained for
each context. However, retraining networks takes resources and might ruin
performance on previous tasks. This work examines the adaptability of DPS
metrics by training ImageNet pretrained CNNs to measure similarity according to
given contexts. Contexts are created by randomly ranking six image distortions.
Distortions later in the ranking are considered more disruptive to similarity
when applied to an image for that context. This also gives insight into whether
the pretrained features capture different similarity contexts. The adapted
metrics are evaluated on a perceptual similarity dataset to evaluate if
adapting to a ranking affects their prior performance. The findings show that
DPS metrics can be adapted with high performance. While the adapted metrics
have difficulties with the same contexts as baselines, performance is improved
in 99% of cases. Finally, it is shown that the adaption is not significantly
detrimental to prior performance on perceptual similarity. The implementation
of this work is available online:
https://github.com/LTU-Machine-Learning/Analysis-of-Deep-Perceptual-Loss-Networks
