AffecTube - Chrome extension for YouTube video affective annotations

被引:0
|
作者
Kulas, Daniel [1 ]
Wrobel, Michal R. [1 ]
机构
[1] Gdansk Univ Technol, Fac Elect Telecommun & Informat, Ul Narutowicza 11-12, PL-80233 Gdansk, Poland
关键词
Emotion recognition; Dataset; Video annotation;
D O I
10.1016/j.softx.2023.101504
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The shortage of emotion-annotated video datasets suitable for training and validating machine learning models for facial expression-based emotion recognition stems primarily from the significant effort and cost required for manual annotation. In this paper, we present AffecTube as a comprehensive solution that leverages crowdsourcing to annotate videos directly on the YouTube platform, resulting in ready-to-use emotion-annotated datasets. AffecTube provides a low-resource environment with an intuitive interface and customizable options, making it a versatile tool applicable not only to emotion annotation, but also to various video-based behavioral annotation processes. (c) 2023 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页数:7
相关论文
共 27 条
  • [21] Multimodal Local-Global Attention Network for Affective Video Content Analysis
    Ou, Yangjun
    Chen, Zhenzhong
    Wu, Feng
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 31 (05) : 1901 - 1914
  • [22] Creating a Novel Semantic Video Search Engine Through Enrichment Textual and Temporal Features of Subtitled YouTube Media Fragments
    Farhadi, Babak
    Ghaznavi-Ghoushchi, M. B.
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE 2013), 2013, : 64 - 72
  • [23] Hybrid feature-based analysis of video's affective content using protagonist detection
    Zhu, Yingying
    Tong, Min
    Jiang, Zhengbo
    Zhong, Shenghua
    Tian, Qi
    EXPERT SYSTEMS WITH APPLICATIONS, 2019, 128 : 316 - 326
  • [24] Efficient Decoding of Affective States from Video-elicited EEG Signals: An Empirical Investigation
    Latifzadeh, Kayhan
    Gozalpour, Nima
    Traver, V. Javier
    Ruotsalo, Tuukka
    Kawala-Sterni, Aleksandra
    Leiva, Luis A.
    ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2024, 20 (10)
  • [25] Video Affective Content Analysis Based on Multimodal Features Using a Novel Hybrid SVM-RBM Classifier
    Ashwin, T. S.
    Saran, Sai
    Reddy, G. Ram Mohana
    2016 IEEE UTTAR PRADESH SECTION INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND ELECTRONICS ENGINEERING (UPCON), 2016, : 416 - 421
  • [26] Deep Learning Classification of Neuro-Emotional Phase Domain Complexity Levels Induced by Affective Video Film Clips
    Aydin, Serap
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 24 (06) : 1695 - 1702
  • [27] Approach to Semi-Automatic Labeling of Video Sequences for Affective Computing - Enabling the Comprehensive Assessment of Emotion Detection Software from Mimics -
    Boehm, Thilo
    Engel, Felix
    Bzdok, Danilo
    Schneider, Frank
    Hemmje, Matthias
    2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2017, : 1996 - 2000