  Recent work in Machine Learning and Computer Vision has highlighted the
presence of various types of systematic flaws inside ground truth object
recognition benchmark datasets. Our basic tenet is that these flaws are rooted
in the many-to-many mappings which exist between the visual information encoded
in images and the intended semantics of the labels annotating them. The net
consequence is that the current annotation process is largely under-specified,
thus leaving too much freedom to the subjective judgment of annotators. In this
paper, we propose vTelos, an integrated Natural Language Processing, Knowledge
Representation, and Computer Vision methodology whose main goal is to make
explicit the (otherwise implicit) intended annotation semantics, thus
minimizing the number and role of subjective choices. A key element of vTelos
is the exploitation of the WordNet lexico-semantic hierarchy as the main means
for providing the meaning of natural language labels and, as a consequence, for
driving the annotation of images based on the objects and the visual properties
they depict. The methodology is validated on images populating a subset of the
ImageNet hierarchy.
