31 Luca Piras Giorgio Giacinto 2010 K-Nearest Neighbors Directed Synthetic Images Injection 11th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2010) Desenzano del Garda, Italy 12/04/2010 cbir00 <div style="text-align: justify"> It is widely acknowledged that good performances of content-based image retrieval systems can be attained by adopting relevance feedback mechanisms. One of the main difficulties in exploiting relevance information is the availability of few relevant images, as users typically label a few dozen of images, the majority of them often being non-relevant to user’s needs. In order to boost the learning capabilities of relevance feedback techniques, this paper proposes the creation of points in the feature space which can be considered as representation of relevant images. The new points are generated taking into account not only the available relevant points in the feature space, but also the relative positions of non-relevant ones. This approach has been tested on a relevance feedback technique, based on the Nearest-Neighbor classification paradigm. Reported experiments show the effectiveness of the proposed technique relatively to precision and recall. </div>