VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method

The fast evolution of digital video has brought many new multimedia applications and, as a consequence, has increased the amount of research into new technologies that aim at improving the effectiveness and efficiency of video acquisition, archiving, cataloging and indexing, as well as increasing the usability of stored videos. Among possible research areas, video summarization is an important topic that potentially enables faster browsing of large video collections and also more efficient content indexing and access. Essentially, this research area consists of automatically generating a short summary of a video, which can either be a static summary or a dynamic summary. In this paper, we present VSUMM, a methodology for the production of static video summaries. The method is based on color feature extraction from video frames and k-means clustering algorithm. As an additional contribution, we also develop a novel approach for the evaluation of video static summaries. In this evaluation methodology, video summaries are manually created by users. Then, several user-created summaries are compared both to our approach and also to a number of different techniques in the literature. Experimental results show – with a confidence level of 98% – that the proposed solution provided static video summaries with superior quality relative to the approaches to which it was compared.

Research highlights
► VSUMM presents the advantages of the main concepts of related works.
► The consistency of results across databases with different characteristics.
► Novel evaluation: quantifies the summary quality, allows more objective comparisons.
► The experiments showed that the users preferred to create more extensive summaries.
► To download (database, video/user summary, etc.): http://www.npdi.dcc.ufmg.br/VSUMM.