Abstract:
Video data is increasing rapidly along with the capacity of storage devices owned by a lay user. Users have moderate to large personal collections of videos and would like to keep them in an organized manner based on its content. Video organizing tools for personal users are way behind even the primitive image organizing tools. We present a mechanism in this paper to help ordinary users organize their personal collection of videos based on categories they choose. We cluster the PHOG features extracted from selected key frames to form a representation for each user-selected category during the learning phase. During the organization phase, labels from a K-NN classifier on these cluster centres for each key frame are aggregated to give a label to the video while categorizing. Video processing is computationally intensive. To perform the computationally intensive steps involved, we exploit the CPU as well as the GPU that is common even on personal systems. Effective use of the parallel hardware on the system is the only way to make the tool scale reasonably to large collections that will be available soon. Our tool is able to organize a set of 100 sport videos of total duration of 1375 minutes in about 9.5 minutes. The process of learning the categories from 12 annotated videos of duration 165 minutes took 75 seconds on a GTX 580 card. These were on a standard desktop with an off-the-shelf GPU. The labeling accuracy is about 96% on all videos.

Paper available at IEEE.