Multiscale summarization and action ranking in egocentric videos

Abstract

Useful information extraction from egocentric videos has evolved as an important research problem for both computer vision and multimedia communities. In this paper, we have addressed two problems, namely (i) generating multiscale summaries, i.e., multiple summaries of different lengths and (ii) priority-based ranking of various actions present in egocentric videos. A new algorithm, termed as Multiscale Egocentric Video Summarization and Action Ranking (MEVSAR), with agglomerative clustering as its backbone, is proposed to solve the above problems. Importantly, the MEVSAR algorithm follows an “analyze once, generate many” principle to generate multiple summaries in a single run and subsequently rank actions from the generated summaries. Experimental evaluation on two well-known publicly available datasets clearly demonstrate the merits of the proposed approach.