Video news classification for automatic content personalization
Marcelo Garcia Manzato, Rudinei Goularte · 2008
With the development of content-based multimedia services, the personalization task has become increasingly important. There is a need for semantic information knowledge, extracted from multimedia streams, in order to achieve the benefits of automatic matching user preferences with multimedia content meaning. Text-based classification techniques may be used in closed-captions captured from news programs, which can define the subject of each piece of news. Latent Semantic Indexing (LSI)-based systems are widely used for classification tasks; however, some drawbacks of the technique may impose limitations, mainly when considering multiple collections. In this paper, we compare an LSI implementation with a Genetic Algorithm (GA)-based system which was designed with the same objective. The classification is made based on high level semantic information extracted from the news video streams. We show that the GA alternative achieves better results when used to automatically classify pieces of news video programs.