  In this paper we propose to represent a scene as an abstraction of 'things'.
We start from 'things' as generated by modern object proposals, and we
investigate their immediately observable properties: position, size, aspect
ratio and color, and those only. Where the recent successes and excitement of
the field lie in object identification, we represent the scene composition
independent of object identities. We make three contributions in this work.
First, we study simple observable properties of 'things', and call it things
syntax. Second, we propose translating the things syntax in linguistic abstract
statements and study their descriptive effect to retrieve scenes. Thirdly, we
propose querying of scenes with abstract block illustrations and study their
effectiveness to discriminate among different types of scenes. The benefit of
abstract statements and block illustrations is that we generate them directly
from the images, without any learning beforehand as in the standard attribute
learning. Surprisingly, we show that even though we use the simplest of
features from 'things' layout and no learning at all, we can still retrieve
scenes reasonably well.
