Commercial mining basedon temporal recurrence hashing algorithm and bag-of-fingerprints model

We propose two novel algorithms for fully-unsupervised, super-fast, and cross-channel TV commercial mining in this paper. The tasks involved in the process include: 1) mining commercial clusters from streams of individual channels, and 2) grouping identical commercial clusters across multiple channels. The first process is achieved with a dual-stage hashing algorithm, which searches for recurring short segments by hashing frames, and it assembles these short segments into sets of commercial sequences by hashing temporal recurrences. The algorithm mined commercials from a one-month stream in less than 42 minutes, which was ten times faster than that in related studies. A new bag-of-fingerprints model is proposed for the second process to encode the temporal clues of local fingerprints. The model is abundantly robust against framing and fingerprinting errors in recurring sequences, and discovers false matches of local fingerprints. A five-month database was used for comprehensively demonstrating the effectiveness and efficiency of the model.