  This work proposes a system for retrieving clothing and fashion products from
video content. Although films and television are the perfect showcase for
fashion brands to promote their products, spectators are not always aware of
where to buy the latest trends they see on screen. Here, a framework for
breaking the gap between fashion products shown on videos and users is
presented. By relating clothing items and video frames in an indexed database
and performing frame retrieval with temporal aggregation and fast indexing
techniques, we can find fashion products from videos in a simple and
non-intrusive way. Experiments in a large-scale dataset conducted here show
that, by using the proposed framework, memory requirements can be reduced by
42.5X with respect to linear search, whereas accuracy is maintained at around
90%.
