The role of classifiers in multimedia content management

Abstract

Enabling semantic detection and indexing is an important task in multimedia content management. Learning and classification techniques are increasingly relevant to the state of the art content management systems. Prom relevance feedback to semantic detection, there is a shift in the amount of supervision that precedes retrieval from light weight classifiers to heavy weight classifiers. In this paper we compare the performance of some popular classifiers for semantic video indexing. We mainly compare among other techniques, one technique for generative modeling and one for discriminant learning and show how they behave depending on the number of examples that the user is willing to provide to the system. We report results using the NIST TREC Video Corpus.