Clarification of Video Retrieval Query Results by the Automated Insertion of Supporting Shots

TL;DR

This paper presents a generic video editing strategy based on cinema narrative principles, supported by a database of scripts, to improve video retrieval query results through automated insertion of supporting shots.

Contribution

It introduces a novel, cinema theory-based, generic editing approach that leverages annotated video scripts for flexible, automated video editing and retrieval enhancement.

Findings

Demonstrates the effectiveness of the generic editing strategy in automated systems

Shows how cinema narrative principles can be applied to computational video editing

Provides algorithms for implementing the editing strategy

Abstract

Computational Video Editing Systems output video generally follows a particular form, e.g. conversation or music videos, in this way they are domain specific. We describe a recent development in our video annotation and segmentation system to support general computational video editing in which we derive a single generic editing strategy from general cinema narrative principles instead of using a hierarchical film gram-mar. We demonstrate how this single principle coupled with a database of scripts derived from annotated videos leverages the existing video editing knowledge encoded within the editing of those sequences in a flexible and generic manner. We discuss the cinema theory foundations for this generic editing strategy, review the algorithms used to effect it, and goon by means of examples to show its appropriateness in an automated system.