Tiny Videos: Non-parametric Content-Based Video Retrieval and Recognition

This work extends the tiny images techniques developed by Torralba et al. to videos. A dataset of 6,612 videos was collected from YouTube in the Sports and News sections. We present a method for compressing the temporal dimension nonuniformly using affinity propagation. We show that nonuniform sampling using affinity propagation outperforms temporal sampling at uniform intervals, because it covers a greater range of visual appearances in the video for the same number of samples. We examine two main applications for the tiny video dataset: duplicate video detection and related video retrieval. We also show that the scope of text-based searches on YouTube can be significantly increased by incorporating visual similarity.