  Human perception is routinely assessing the similarity between images, both
for decision making and creative thinking. But the underlying cognitive process
is not really well understood yet, hence difficult to be mimicked by computer
vision systems. State-of-the-art approaches using deep architectures are often
based on the comparison of images described as feature vectors learned for
image categorization task. As a consequence, such features are powerful to
compare semantically related images but not really efficient to compare images
visually similar but semantically unrelated. Inspired by previous works on
neural features adaptation to psycho-cognitive representations, we focus here
on the specific task of learning visual image similarities when analogy
matters. We propose to compare different supervised, semi-supervised and
self-supervised networks, pre-trained on distinct scales and contents datasets
(such as ImageNet-21k, ImageNet-1K or VGGFace2) to conclude which model may be
the best to approximate the visual cortex and learn only an adaptation function
corresponding to the approximation of the the primate IT cortex through the
metric learning framework. Our experiments conducted on the Totally Looks Like
image dataset highlight the interest of our method, by increasing the retrieval
scores of the best model @1 by 2.25x. This research work was recently accepted
for publication at the ICIP 2021 international conference [1]. In this new
article, we expand on this previous work by using and comparing new pre-trained
feature extractors on other datasets.
