Semantic home video categorization

Nowadays, a strong need exists for the efficient organization of an increasing amount of home video content. To create an efficient system for the management of home video content, it is required to categorize home video content in a semantic way. So far, a significant amount of research has already been dedicated to semantic video categorization. However, conventional categorization approaches often rely on unnecessary concepts and complicated algorithms that are not suited in the context of home video categorization. To overcome the aforementioned problem, this paper proposes a novel home video categorization method that adopts semantic home photo categorization. To use home photo categorization in the context of home video, we segment video content into shots and extract key frames that represent each shot. To extract the semantics from key frames, we divide each key frame into ten local regions and extract lowlevel features. Based on the low level features extracted for each local region, we can predict the semantics of a particular key frame. To verify the usefulness of the proposed home video categorization method, experiments were performed with home video sequences, labeled by concepts part of the MPEG-7 VCE2 dataset. To verify the usefulness of the proposed home video categorization method, experiments were performed with 70 home video sequences. For the home video sequences used, the proposed system produced a recall of 77% and an accuracy of 78%.