  In Machine Learning and Robotics, the semantic content of visual features is
usually provided to the system by a human who interprets its content. On the
contrary, strictly unsupervised approaches have difficulties relating the
statistics of sensory inputs to their semantic content without also relying on
prior knowledge introduced in the system. We proposed in this paper to tackle
this problem from a sensorimotor perspective. In line with the Sensorimotor
Contingencies Theory, we make the fundamental assumption that the semantic
content of sensory inputs at least partially stems from the way an agent can
actively transform it. We illustrate our approach by formalizing how simple
visual features can induce invariants in a naive agent's sensorimotor
experience, and evaluate it on a simple simulated visual system. Without any a
priori knowledge about the way its sensorimotor information is encoded, we show
how an agent can characterize the uniformity and edge-ness of the visual
features it interacts with.
