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

Soroosh Poorgholi*, Osman Semih Kayhan, Jan C. van Gemert

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationPattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
EditorsAlberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
Number of pages17
ISBN (Print)9783030687984
Publication statusPublished - 2021
Event25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Virtual, Online, Italy
Duration: 10 Jan 202115 Jan 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12664 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Pattern Recognition Workshops, ICPR 2020
CityVirtual, Online


  • Action recognition
  • t-SNE
  • Video annotation


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