  One of the prevalent learning tasks involving images is content-based image
classification. This is a difficult task especially because the low-level
features used to digitally describe images usually capture little information
about the semantics of the images. In this paper, we tackle this difficulty by
enriching the semantic content of the image representation by using external
knowledge. The underlying hypothesis of our work is that creating a more
semantically rich representation for images would yield higher machine learning
performances, without the need to modify the learning algorithms themselves.
The external semantic information is presented under the form of non-positional
image labels, therefore positioning our work in a weakly supervised context.
Two approaches are proposed: the first one leverages the labels into the visual
vocabulary construction algorithm, the result being dedicated visual
vocabularies. The second approach adds a filtering phase as a pre-processing of
the vocabulary construction. Known positive and known negative sets are
constructed and features that are unlikely to be associated with the objects
denoted by the labels are filtered. We apply our proposition to the task of
content-based image classification and we show that semantically enriching the
image representation yields higher classification performances than the
baseline representation.
