Full-Reference Quality Assessment for Video Summary

As video summarization techniques have attracted more and more attention for efficient multimedia data management, quality assessment of video summary is required. To address the lack of automatic evaluation techniques, this paper proposes a novel framework including several new algorithms to assess the quality of the video summary against a given reference. First, we partition the reference video summary and the candidate video summary into the sequences of summary unit (SU). Then, we utilize alignment based algorithm to match the SUs in the candidate summary with the SUs in the corresponding reference summary. Third, we propose a novel similarity based 4 C - assessment algorithm to evaluate the candidate video summary from the perspective of coverage, conciseness, coherence, and context, respectively. Finally, the individual assessment results are integrated according to userpsilas requirement by a learning based weight adaptation method. The proposed framework and techniques are experimented on a standard dataset of TRECVID 2007 and show the good performance in automatic video summary assessment.