Contented-Based Large Scale Web Audio Copy Detection

The exponential growth of web videos brings content based copy detection into a crucial issue. Besides the image information, audio also plays an important role in copy detection. In this paper, the audio-based copy detection framework is introduced. Three contributions are presented: (1) the band energy difference based feature is improved by adding multiscale information, which extends the candidate feature sets; (2) a conditional entropy based method is used to select 16 ordinal relations to generate a more compact and robust feature combination among the random C₉₁¹⁶ ≈ 2.6 × 10¹⁷ combinations; (3) the result-based fusion strategy is introduced to recall the missed true positives. The proposed algorithm outperforms the traditional coarse fingerprints, shown by experiments conducted in the TRECVID 2011 Content-based Copy Detection (CCD) database.