t-EVA: Time-Efficient t-SNE Video Annotation

• URL: http://arxiv.org/abs/2011.13202v1
• Date: Thu, 26 Nov 2020 09:56:54 GMT
• Title: t-EVA: Time-Efficient t-SNE Video Annotation
• Authors: Soroosh Poorgholi, Osman Semih Kayhan and Jan C. van Gemert
• Abstract summary: t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification. We show that t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification.
• Score: 16.02592287695421
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Video understanding has received more attention in the past few years due to the availability of several large-scale video datasets. However, annotating large-scale video datasets are cost-intensive. In this work, we propose a time-efficient video annotation method using spatio-temporal feature similarity and t-SNE dimensionality reduction to speed up the annotation process massively. Placing the same actions from different videos near each other in the two-dimensional space based on feature similarity helps the annotator to group-label video clips. We evaluate our method on two subsets of the ActivityNet (v1.3) and a subset of the Sports-1M dataset. We show that t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification.

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