2023-05-242023-05-242011-06978-84-615-5513-0https://hdl.handle.net/11441/146582A 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.application/pdf3engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess