@inproceedings{9cdee259377f49c9be55f8f204be7a4c,
title = "t-EVA: Time-Efficient t-SNE Video Annotation",
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 (https://github.com/spoorgholi74/t-EVA ) can outperform other video annotation tools while maintaining test accuracy on video classification.",
keywords = "Action recognition, t-SNE, Video annotation",
author = "Soroosh Poorgholi and Kayhan, {Osman Semih} and {van Gemert}, {Jan C.}",
year = "2021",
doi = "10.1007/978-3-030-68799-1_12",
language = "English",
isbn = "9783030687984",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science+Business Media",
pages = "153--169",
editor = "{Del Bimbo}, Alberto and Rita Cucchiara and Stan Sclaroff and Farinella, {Giovanni Maria} and Tao Mei and Marco Bertini and Escalante, {Hugo Jair} and Roberto Vezzani",
booktitle = "Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings",
note = "25th International Conference on Pattern Recognition Workshops, ICPR 2020 ; Conference date: 10-01-2021 Through 15-01-2021",
}