Multimedia Content Classification in the Cloud

Abstract

Information extraction from multimedia content is a challenging task. In this thesis, we present an architecture of multimedia contents classification system that provides different phases to extract semantic information from broadcasted streams, starting with the segmentation process, news topics extraction, and advertisement detection and classification. Next, we give an extension to our framework and describes an audio-based hybrid model for content classification combining different deep neural networks with auto-encoder applied to advertisement detection in TV broadcast. Our models achieve high levels of precision. The last contribution consists of a distributed architecture based on the Kafka and Spark frameworks which offer parallel processing of TV streams, we demonstrate through this work the scalability and robustness of this architecture.