A Video Similarity Measure Combining Alignment, Graphical and Speech Features

Description

A large volume of video content on the web is available today, which demands efficient manage­ment. To effectively manage, search, retrieve and copy detection, similarity methods play a critica] role. In this paper, a novel video similarity mea­sure using visual fcatures, alignment distances and speech transcripts is proposed. Video files are rep­resented by a sequence of segments set where each segment contains color histograms, start time and a set of syllables extracted from the speech in the au­dio track. In a first step, textual, alignment and vi­sual features are extracted. They complement each other and can be further combined to boost the seg­ment similarity. The second step describes how the Maximum Bipaitite Matching and sorne statisti­cal features are. applied to find segments crnTespon­dences and calculate a global similarity value re­spectively. Experiments far video similarity were performed on a dataset and promising results were achieved to demonstrate the effectiveness of this method.

Abstract

Ministerio de Ciencia e Innovación TIN2009-14378- C02-01

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