Fast Seriation of Multiple Homogeneous-content Videos Using Audio-visual Features

Facing a large number of homogeneous-content videos shared on the internet, it is essential to seriate videos with prior information. In this paper, we propose fast video seriation (ViSA) scheme based on audio-visual features. The features, mel-frequency cepstral coefficients (MFCCs) and color histogram are extracted as audio feature and visual feature, respectively. Principal component analysis (PCA) is exploited for dimensionality reduction as well as time consumption reducing during feature matching. Subsequently, the proposed fast feature matching computes difference between features, in the meantime, estimates time difference between two videos. The chronological ordering establishes relationship among videos, and then seriates videos by synchronizing video time. The experiment results will show the proposed scheme reduces more 50% computing time than brute force approach does in feature matching. The error of time difference between groundtruth and the estimated result is very small. The experiment results will demonstrate that the proposed scheme is efficient in video seriating.