SciELO - Scientific Electronic Library Online

 
 número47Recommending Machine Translation Output to Translators by Estimating Translation Effort: A Case StudyEfficient Routing of Mobile Agents in a Stochastic Network índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Polibits

versão On-line ISSN 1870-9044

Polibits  no.47 México Jan./Jul. 2013

 

Scene Boundary Detection from Movie Dialogue: A Genetic Algorithm Approach

 

Amitava Kundu1, Dipankar Das2, and Sivaji Bandyopadhyay1

 

1 Department of Computer Science & Engineering, Jadavpur University, Kolkata-700032, India (e-mail: amitava.jucse@gmail.com, vajiJu_cse@yahoo.com).

2 Department of Computer Science & Engineering, National Institue of Technology, Meghalaya, Laitumkhrah, Shillong-793003, Meghalaya, India (email: dipankar.dipnil2005@gmail.com).

 

Manuscript received December 15, 2012.
Manuscript accepted for publication January 11, 2013.

 

Abstract:

Movie scripts are a rich textual resource that can be tapped for movie content analysis. This article describes a mechanism for fragmenting a sequence of movie script dialogue into scene-wise groups. In other words, it attempts to locate scene transitions using information acquired from a sequence of dialogue units. We collect movie scripts from a web archive. Thereafter, we preprocess them to develop a resource of dialogues. We feed the dialogue sequence from a script to a Genetic Algorithm (GA) framework. The system fragments the sequence into adjacent groups of dialogue units or output 'scenes'. We use SentiWordnet scores and Wordnet distance for dialogue units to optimize this grouping so that adjacent scenes are semantically most dissimilar. Then we compare the resulting fragmented dialogue sequence with the original scene-wise alignment of dialogue in the script.

Key words: Dialogue, genetic algorithm, movie script, scene.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

REFERENCES

[1] J. J. Jung, E. You, and S. B. Park, "Emotion-based character clustering for managing story-based contents: a cinemetric analysis," Multimedia Tools and Applications, pp. 1-17, 2012.         [ Links ]

[2] G. I. Lin, and M. A. Walker, "All the world's a stage: Learning character models from film," In Proceedings of the Seventh AI and Interactive Digital Entertainment Conference, AIIDE, vol. 11, 2011.         [ Links ]

[3] M. A. Walker, G. I. Lin, and J. E. Sawyer, "An Annotated Corpus of Film Dialogue for Learning and Characterizing Character Style," Proceedings of LREC, 2012.         [ Links ]

[4] R. E. Banchs, "Movie-DiC: a movie dialogue corpus for research and development," In Proc. of the 50th Annual Meeting of the ACL. 2012, 2012.         [ Links ]

[5] M. Cooper, and J. Foote, "Scene boundary detection via video self-similarity analysis," International Conference on Image Processing, vol. 3, pp. 378-381, IEEE, 2001.         [ Links ]

[6] J. Wang, and T. S. Chua, "A cinematic-based framework for scene boundary detection in video," The Visual Computer, vol. 19, no.5, 2003, pp-329-341.         [ Links ]

[7] T. Cour, C. Jordan, E. Miltsakaki, and B. Taskar, "Movie/script:Alignment and parsing of video and text transcription," Proc. 10th European Conf. Computer Vision, 2008, pp. 158-171.         [ Links ]

[8] S. B. Park, H. N. Kim, H. Kim, and G. S. Jo, "Exploiting Script-Subtitles Alignment to Scene Boundary Detection in Movie," In Multimedia (ISM), 2010 IEEE International Symposium on, pp. 49-56. IEEE (2010)        [ Links ]

[9] R. Turetsky, and N. Dimitrova, "Screenplay alignment for closed system speaker identification and analysis of feature films," Proc. IEEE Int. Conf. Multimedia andExpo (ICME'04), 2004, pp. 1659-1662.         [ Links ]

[10] D. E. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning," Addi-son-Wesley, New York, 1989.         [ Links ]

[11] L. (Ed.) Davis, "Handbook of Genetic Algorithms," Van Nostrand Reinhold, New York, 1991.         [ Links ]

[12] A. Jhala, "Exploiting Structure and Conventions of Movie Scripts for Information Retrieval and Text Mining," Interactive Storytelling, 2008, pp. 210-213.         [ Links ]

[13] B. Stefano, A. Esuli, and F. Sebastiani, "Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining," InProceedings of the Seventh conference on International Language Resources and Evaluation (LREC'IO), Valletta, Malta, May. European Language Resources Association (ELRA), 2010.         [ Links ]

[14] M. C. De Marneffe, C. D. Manning, "Stanford typed dependencies manual," nlp.stan-ford.edu/software/dependencies_manual.pdf, 2008.         [ Links ]

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons