Large-scale web video shot ranking based on visual features and tag co-occurrence

Authors:
Do Hang Nga;Keiji Yanai
Affiliations:
The University of Electro-Communications, Tokyo, Tokyo, Japan;The University of Electro-Communications, Tokyo, Tokyo, Japan
Venue:
Proceedings of the 21st ACM international conference on Multimedia
Year:
2013

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Abstract

In this paper, we propose a novel ranking method, VisualTextualRank, which extends [1] and [2]. Our method is based on random walk over bipartite graph to integrate visual information of video shots and tag information of Web videos effectively. Note that instead of treating the textual information as an additional feature for shot ranking, we explore the mutual reinforcement between shots and textual information of their corresponding videos to improve shot ranking. We apply our proposed method to the system of extracting automatically relevant video shots of specific actions from Web videos [3]. Based on our experimental results, we demonstrate that our ranking method can improve the performance of video shot retrieval.