  We share the implementation details and testing results for video retrieval
system based exclusively on features extracted by convolutional neural
networks. We show that deep learned features might serve as universal signature
for semantic content of video useful in many search and retrieval tasks. We
further show that graph-based storage structure for video index allows to
efficiently retrieving the content with complicated spatial and temporal search
queries.
