Diversity ranking for video retrieval from a broadcaster archive

Abstract

Video retrieval through text queries is a very common practice in broadcaster archives. The query keywords are compared to the metadata labels that documentalists have previously associated to the video assets. This paper focuses on a ranking strategy to obtain more relevant keyframes among the top hits of the results ranked lists but, at the same time, keeping a diversity of video assets. Previous solutions based on a random walk over a visual similarity graph have been modified to increase the asset diversity by filtering the edges between keyframes depending on their asset. The random walk algorithm is applied separately for ever visual feature to avoid any normalization issue between visual similarity metrics. Finally, this work evaluates performance with two separate metrics: the relevance is measured by the Average Precision and the diversity is assessed by the Average Diversity, a new metric presented in this work.