Temporal recurrence hashing algorithm for mining commercials from multimedia streams

We propose a dual-stage algorithm for fully-unsupervised and super-fast TV commercial mining in this paper. The two stages involved in process include: 1) searching for recurring short segments, and 2) assembling these short segments into sets of long and complete commercial sequences. The first stage is achieved by frame hashing. Different from the related studies that depend on brute-force pairwise matching, we propose applying a second-stage hashing algorithm for the recurring segment assemblage, which is the key idea in this paper. A large-scale archive containing a 10-hour and a 1-month stream was used for the experimentation. The algorithm mined commercials from the 1-month stream in less than 50 minutes, which was ten times faster than that of related studies, with a 98.05% sequence-level and 97.39% frame-level accuracy. We demonstrate the performance consistency of the algorithm on both audio and video streams, and investigate the computational cost from both the theoretical and experimental viewpoints.