Multimodal video concept classification

The multimedia data has been continuously produced and shared out at a high rate as a result of the internet usage escalation. Thus, the size of multimedia data has rapidly increased, and hence, automated methods are needed to analyze the contents of the data produced. Video data is an important component of multimedia data. Video content analysis is an important research topic for several applications, such as audio-video based surveillance, content-based search and retrieval and can be defined as the automatic determination of temporal or spatial events/concepts in content of video data. Video content analysis is a difficult task due to the complex nature of the video content and requires efficient algorithms for extraction of high-level information included in the content. The increasing size of video data makes this task more difficult. In this thesis, a method based on the fusion of audio-visual modalities for multimodal content analysis of video data is proposed and implemented on a big data platform. The proposed method is based on the fusion of representations of Mel-frequency Cepstral Coefficient (MFCC) features with Convolutional Neural Network (CNN) features. The proposed method is implemented on Apache Spark big data platform. The success of the proposed method is evaluated on the TRECVID 2012 SIN data set. Our results show that the multi-modal method improves the accuracy of the single-model approach and also the big data platform significantly reduces the computation time of the multi-modal video content analysis method.