Framework para Segmentação Temporal de Vídeos Educacionais
Resumo
Este artigo discute os principais aspectos relacionados aos frameworks de segmentação de vídeos educacionais (videoaulas). Apresenta um framework, denominado EasyTopic, que permite a utilização de diversas abordagens para o processo de segmentação temporal de vídeoaulas em tópicos (segmentos semânticos), de forma configurável e generalista. Com o objetivo de comprovar o funcionamento do framework, apresenta seu uso na implementação de uma solução para o problema de segmentação em tópicos de vídeos educacionais, com resultados e considerações sobre o uso do framework.
Palavras-chave:
videoaulas, segmentação de vídeos, segmentação temporal
Referências
Biswas, A., Gandhi, A., and Deshmukh, O. (2015). Mmtoc: A multimodal method for table of content creation in educational videos. In Proceedings of the 23rd ACM international conference on Multimedia, pages 621–630. ACM.
Che, X., Yang, H., and Meinel, C. (2018). Automatic online lecture highlighting based on multimedia analysis. IEEE Transactions on Learning Technologies, 11(1):27–40.
Davila, K. and Zanibbi, R. (2017). Whiteboard video summarization via spatio-temporal conflict minimization. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, pages 355–362. IEEE.
Galanopoulos, D. and Mezaris, V. (2019). Temporal lecture video fragmentation using word embeddings. In International Conference on Multimedia Modeling, pages 254–265. Springer.
Goutte, C. and Gaussier, E. (2005). A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In European Conference on Information
Retrieval, pages 345–359. Springer.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., and Altman, D. G. (2016). Statistical tests, p values, confidence intervals, and power: a
guide to misinterpretations. European journal of epidemiology, 31(4):337–350.
Lakens, D. (2017). Equivalence tests: a practical primer for tests, correlations, and meta-analyses. Social psychological and personality science, 8(4):355–362.
Lin, M., Chau, M., Cao, J., and Nunamaker Jr, J. F. (2005). Automated video segmentation for lecture videos: A linguistics-based approach. International Journal of Technology and Human Interaction (IJTHI), 1(2):27–45.
Mahapatra, D., Mariappan, R., and Rajan, V. (2018). Automatic hierarchical table of contents generation for educational videos. In Companion of the The Web Conference 2018 on The Web Conference 2018, pages 267–274. International World Wide Web Conferences Steering Committee.
Mitra, U. and Srivastava, G. (2020). A study on agent-based web searching and information retrieval. In Intelligent Communication, Control and Devices, pages 569–578. Springer.
Pavel, A., Hartmann, B., and Agrawala, M. (2014). Video digests: a browsable, skimmable format for informational lecture videos. In Proceedings of the 27th annual ACM symposium on User interface software and technology, pages 573–582. ACM.
Ronchetti, M. (2010). Using video lectures to make teaching more interactive. International Journal of Emerging Technologies in Learning (iJET), 5(2).
Shah, R. R., Yu, Y., Shaikh, A. D., Tang, S., and Zimmermann, R. (2014). Atlas: automatic temporal segmentation and annotation of lecture videos based on modelling transition time. In Proceedings of the 22nd ACM international conference on Multimedia, pages 209–212. ACM.
Soares, E. R. and Barrere, E. (2019). An optimization model for temporal video lecture segmentation using word2vec and acoustic features. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, pages 513–520.
Tuna, T., Joshi, M., Varghese, V., Deshpande, R., Subhlok, J., and Verma, R. (2015).
Topic based segmentation of classroom videos. In 2015 IEEE Frontiers in Education Conference (FIE), pages 1–9. IEEE.
Yang, H. and Meinel, C. (2014). Content based lecture video retrieval using speech and video text information. IEEE Transactions on Learning Technologies, 7(2):142–154.
Zhang, Y., Zhang, J., and Zhang, D. (2009). Implementing and testing producer-consumer problem using aspect-oriented programming. In 2009 Fifth International Conference on Information Assurance and Security, volume 2, pages 749–752. IEEE.
Che, X., Yang, H., and Meinel, C. (2018). Automatic online lecture highlighting based on multimedia analysis. IEEE Transactions on Learning Technologies, 11(1):27–40.
Davila, K. and Zanibbi, R. (2017). Whiteboard video summarization via spatio-temporal conflict minimization. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, pages 355–362. IEEE.
Galanopoulos, D. and Mezaris, V. (2019). Temporal lecture video fragmentation using word embeddings. In International Conference on Multimedia Modeling, pages 254–265. Springer.
Goutte, C. and Gaussier, E. (2005). A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In European Conference on Information
Retrieval, pages 345–359. Springer.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., and Altman, D. G. (2016). Statistical tests, p values, confidence intervals, and power: a
guide to misinterpretations. European journal of epidemiology, 31(4):337–350.
Lakens, D. (2017). Equivalence tests: a practical primer for tests, correlations, and meta-analyses. Social psychological and personality science, 8(4):355–362.
Lin, M., Chau, M., Cao, J., and Nunamaker Jr, J. F. (2005). Automated video segmentation for lecture videos: A linguistics-based approach. International Journal of Technology and Human Interaction (IJTHI), 1(2):27–45.
Mahapatra, D., Mariappan, R., and Rajan, V. (2018). Automatic hierarchical table of contents generation for educational videos. In Companion of the The Web Conference 2018 on The Web Conference 2018, pages 267–274. International World Wide Web Conferences Steering Committee.
Mitra, U. and Srivastava, G. (2020). A study on agent-based web searching and information retrieval. In Intelligent Communication, Control and Devices, pages 569–578. Springer.
Pavel, A., Hartmann, B., and Agrawala, M. (2014). Video digests: a browsable, skimmable format for informational lecture videos. In Proceedings of the 27th annual ACM symposium on User interface software and technology, pages 573–582. ACM.
Ronchetti, M. (2010). Using video lectures to make teaching more interactive. International Journal of Emerging Technologies in Learning (iJET), 5(2).
Shah, R. R., Yu, Y., Shaikh, A. D., Tang, S., and Zimmermann, R. (2014). Atlas: automatic temporal segmentation and annotation of lecture videos based on modelling transition time. In Proceedings of the 22nd ACM international conference on Multimedia, pages 209–212. ACM.
Soares, E. R. and Barrere, E. (2019). An optimization model for temporal video lecture segmentation using word2vec and acoustic features. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, pages 513–520.
Tuna, T., Joshi, M., Varghese, V., Deshpande, R., Subhlok, J., and Verma, R. (2015).
Topic based segmentation of classroom videos. In 2015 IEEE Frontiers in Education Conference (FIE), pages 1–9. IEEE.
Yang, H. and Meinel, C. (2014). Content based lecture video retrieval using speech and video text information. IEEE Transactions on Learning Technologies, 7(2):142–154.
Zhang, Y., Zhang, J., and Zhang, D. (2009). Implementing and testing producer-consumer problem using aspect-oriented programming. In 2009 Fifth International Conference on Information Assurance and Security, volume 2, pages 749–752. IEEE.
Publicado
24/11/2020
Como Citar
BARRÉRE, Eduardo; SOUZA, Jairo; SOARES, Eduardo Rocha.
Framework para Segmentação Temporal de Vídeos Educacionais. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 972-981.
DOI: https://doi.org/10.5753/cbie.sbie.2020.972.
