Context-enhanced video understanding

Many recent efforts have been made to automatically index multimedia content with the aim of bridging the semantic gap between syntax and semantics. In this paper, we propose a novel framework to automatically index video using context for video understanding. First we discuss the notion of context and how it relates to video understanding. Then we present the framework we are constructing, which is modeled as an expert system that uses a rule-based engine, domain knowledge, visual detectors (for objects and scenes), and different data sources available with the video (metadata, text from automatic speech recognition, etc.). We also describe our approach to align text from speech recognition and video segments, and present experiments using a simple implementation of our framework. Our experiments show that context can be used to improve the performance of visual detectors.