A similarity measure between videos using alignment, graphical and speech features

A novel video similarity measure is proposed by using visual features, alignment distances and speech transcripts. First, video files are represented by a sequence of segments each of which contains colour histograms, starting time, and a set of phonemes. After, textual, alignment and visual features are extracted of these segments. The following step, bipartite matching and statistical features are applied to find correspondences between segments. Finally, a similarity is calculated between videos. Experiments have been carried out and promising results have been obtained.

► We design a video similarity measure combining speech, alignment and visual features. ► The similarity measure is based on video segments instead of video frames. ► The particular combination of the descriptors can be crucial for different comparisons.