  In this paper, we describe in details VISIONE, a video search system that
allows users to search for videos using textual keywords, occurrence of objects
and their spatial relationships, occurrence of colors and their spatial
relationships, and image similarity. These modalities can be combined together
to express complex queries and satisfy user needs. The peculiarity of our
approach is that we encode all the information extracted from the keyframes,
such as visual deep features, tags, color and object locations, using a
convenient textual encoding indexed in a single text retrieval engine. This
offers great flexibility when results corresponding to various parts of the
query (visual, text and locations) have to be merged. In addition, we report an
extensive analysis of the system retrieval performance, using the query logs
generated during the Video Browser Showdown (VBS) 2019 competition. This
allowed us to fine-tune the system by choosing the optimal parameters and
strategies among the ones that we tested.
