The concept of image similarity is ambiguous, meaning that images that are
considered similar in one context might not be in another. This ambiguity
motivates the creation of metrics for specific contexts. This work explores the
ability of the successful deep perceptual similarity (DPS) metrics to adapt to
a given context. Recently, DPS metrics have emerged using 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 contexts of
similarity. No single metric can suit all definitions of similarity and
previous metrics have been rule-based which are labor intensive to rewrite for
new contexts. DPS metrics, on the other hand, use neural networks which 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 positive scalars for the deep features of pretrained
CNNs to correctly measure similarity for different contexts. Evaluation is
performed on contexts defined by randomly ordering six image distortions (e.g.
rotation) by which should be considered more similar when applied to an image.
This also gives insight into whether the features in the CNN is enough to
discern different distortions without retraining. Finally, the trained metrics
are evaluated on a perceptual similarity dataset to evaluate if adapting to an
ordering affects their performance on established scenarios. 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