  Automatic forensic image analysis assists criminal investigation experts in
the search for suspicious persons, abnormal behaviors detection and identity
matching in images. In this paper we propose a person retrieval system that
uses textual queries (e.g., "black trousers and green shirt") as descriptions
and a one-class generative color model with outlier filtering to represent the
images both to train the models and to perform the search. The method is
evaluated in terms of its efficiency in fulfilling the needs of a forensic
retrieval system: limited annotation, robustness, extensibility, adaptability
and computational cost. The proposed generative method is compared to a
corresponding discriminative approach. Experiments are carried out using a
range of queries in three different databases. The experiments show that the
two evaluated algorithms provide average retrieval performance and adaptable to
new datasets. The proposed generative algorithm has some advantages over the
discriminative one, specifically its capability to work with very few training
samples and its much lower computational requirements when the number of
training examples increases.
