  This work introduces a complexity measure which addresses some conflicting
issues between existing ones by using a new principle - measuring the average
amount of symmetry broken by an object. It attributes low (although different)
complexity to either deterministic or random homogeneous densities and higher
complexity to the intermediate cases. This new measure is easily computable,
breaks the coarse graining paradigm and can be straightforwardly generalised,
including to continuous cases and general networks. By applying this measure to
a series of objects, it is shown that it can be consistently used for both
small scale structures with exact symmetry breaking and large scale patterns,
for which, differently from similar measures, it consistently discriminates
between repetitive patterns, random configurations and self-similar structures.
