  Vector-space word representations obtained from neural network models have
been shown to enable semantic operations based on vector arithmetic. In this
paper, we explore the existence of similar information on vector
representations of images. For that purpose we define a methodology to obtain
large, sparse vector representations of image classes, and generate vectors
through the state-of-the-art deep learning architecture GoogLeNet for 20K
images obtained from ImageNet. We first evaluate the resultant vector-space
semantics through its correlation with WordNet distances, and find vector
distances to be strongly correlated with linguistic semantics. We then explore
the location of images within the vector space, finding elements close in
WordNet to be clustered together, regardless of significant visual variances
(e.g. 118 dog types). More surprisingly, we find that the space unsupervisedly
separates complex classes without prior knowledge (e.g. living things).
Afterwards, we consider vector arithmetics. Although we are unable to obtain
meaningful results on this regard, we discuss the various problem we
encountered, and how we consider to solve them. Finally, we discuss the impact
of our research for cognitive systems, focusing on the role of the architecture
being used.
