Genre classification method for home videos

In this paper, we present a pioneering study on genre classification for home video. Analyzing home video is a challenging problem because it is generally unstructured and of low production quality in audio and video. Our approach is to define a set of genres referring to those in the actual video sharing site, and to extract salient low level features from MPEG compressed data which are robust to low production quality content. Experimental results based on ensemble learning show that our proposed method achieves around 0.7 to 0.8 F-measure values with 37.5 times faster processing than real-time playback for QVGA resolution home video.