  Modern research in content-based image retrieval systems (CIBR) has become
progressively more focused on the richness of human semantics. Several
approaches may be used to reduced the 'semantic gap' between the high-level
human experience and the low level visual features of pictures. Object
ontology, among others, is one of the methods. In this paper we investigate the
use of a codified emotion ontology over global color features of images to
annotate the images at a high semantic level. In order to speed up the
annotation process the images are sampled so that each digital image is
represented by a random subset of its content. We test within controlled
conditions how this random subset may represent the adequate high level
emotional concept presented in the image. We monitor this information reducing
process with entropy measures, showing that controlled random sampling can
capture with significant relevance high level concepts for picture
representation.
