VideoSET: Video Summary Evaluation through Text

Serena Yeung; Alireza Fathi; Li Fei-Fei

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arXiv · 1406.5824

VideoSET: Video Summary Evaluation through Text

Serena Yeung·Alireza Fathi·Li Fei-Fei

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Abstract

In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. Given a video summary, a text representation of the video summary is first generated, and an NLP-based metric is then used to measure its semantic distance to ground-truth text summaries written by humans. We show that our technique has higher agreement with human judgment than pixel-based distance metrics. We also release text annotations and ground-truth text summaries for a number of publicly available video datasets, for use by the computer vision community.

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Serena Yeung, Alireza Fathi, Li Fei-Fei. 2014-06-23. VideoSET: Video Summary Evaluation through Text. https://arxiv.org/abs/1406.5824

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