research-article

NrityaManch: An Annotation and Retrieval System for Bharatanatyam Dance

Authors:

Srijoni Majumdar,

Partha Pratim Das,

K Sreenivas RaoAuthors Info & Claims

FIRE '22: Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation

Pages 65 - 73

https://doi.org/10.1145/3574318.3574338

Published: 12 January 2023 Publication History

Abstract

This paper presents an annotation and retrieval application named NrityaManch dedicated explicitly to the Indian classical dance. We primarily choose Bharatanatyam dance for the application development. We exploit ontology technique which captures dance image’s annotation details and structurally organizes the dance database. An OWL2 ontology is developed in Protégé 5.5.0 which is validated using HermiT 1.4.3.456 reasoner to maintain consistency. A user interface is provided for the manual annotation of dance images. Initially, we focus on dancer details, dance details, and elements of static dance posture like hasta mudra during the annotation. All annotation details are saved in RDF/XML file. A search window is provided, which facilitates two types of search - natural language query search and tight query search. Named Entity Recognition (NER) pipeline mechanism is utilized in this work which facilitates keyword extraction from natural language queries. A SPARQL query is automatically generated by the system which is applied to the RDF corpus in order to retrieve distinct images. The NER pipeline mechanism achieves an accuracy of 80% for our dance dataset. The system achieves an average f-score value of 0.8547 for the retrieval functionality. The proposed system intends to help dance learners to find dance resources in a dedicated place and will also help in Indian classical dance preservation.

References

[1]

Harry W Agius and Marios C Angelides. 2001. Modeling content for semantic-level querying of multimedia. Multimedia Tools and Applications 15, 1 (2001), 5–37.

Digital Library

[2]

Wakana Asahina, Narumi Okada, Naoya Iwamoto, Taro Masuda, Tsukasa Fukusato, and Shigeo Morishima. 2015. Automatic facial animation generation system of dancing characters considering emotion in dance and music. In SIGGRAPH Asia 2015 Posters. 1–1.

[3]

Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Nasir Mahmood, and Waqar Mahmood. 2019. The use of ontology in retrieval: a study on textual, multilingual, and multimedia retrieval. IEEE Access 7(2019), 21662–21686.

[4]

Marco Bertini, Alberto Del Bimbo, and Carlo Torniai. 2005. Enhanced ontologies for video annotation and retrieval. In Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval. 89–96.

Digital Library

[5]

Himadri Bhuyan, Jagadeesh Killi, Jatindra Kumar Dash, Partha Pratim Das, and Soumen Paul. 2022. Motion Recognition in Bharatanatyam Dance. IEEE Access 10(2022), 67128–67139. https://doi.org/10.1109/ACCESS.2022.3184735

[6]

Aurora Castro-Méndez, Desidero Mateos-Martinez, José M Castillo-López, and Alfonso Vargas-Macias. 2022. Evaluation of the Correlation Between Flamenco Dance and Ankle and Foot Posture.Journal of the American Podiatric Medical Association 112, 1(2022).

[7]

Jacky CP Chan, Howard Leung, Jeff KT Tang, and Taku Komura. 2010. A virtual reality dance training system using motion capture technology. IEEE transactions on learning technologies 4, 2 (2010), 187–195.

[8]

Swati Dewan, Shubham Agarwal, and Navjyoti Singh. 2018. Automatic Labanotation Generation, Semi-automatic Semantic Annotation and Retrieval of Recorded Videos. In International Conference on Asian Digital Libraries. Springer, 55–60.

[9]

Katerina El Raheb, Nicolas Papapetrou, Vivi Katifori, and Yannis Ioannidis. 2016. Balonse: Ballet ontology for annotating and searching video performances. In Proceedings of the 3rd International Symposium on Movement and Computing. 1–8.

Digital Library

[10]

Slim Essid, Dimitrios Alexiadis, Robin Tournemenne, Marc Gowing, Philip Kelly, David Monaghan, Petros Daras, Angélique Drémeau, and Noel E O’Connor. 2012. An advanced virtual dance performance evaluator. In 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2269–2272.

[11]

Sangeeta Jadhav, Manish Joshi, and Jyoti Pawar. 2012. Modeling BharataNatyam dance steps: art to SMart. In Proceedings of the CUBE International Information Technology Conference. 320–325.

Digital Library

[12]

Sotiris Karavarsamis, Dimitrios Ververidis, Giannis Chantas, Spiros Nikolopoulos, and Yiannis Kompatsiaris. 2016. Classifying Salsa dance steps from skeletal poses. In 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 1–6.

[13]

Tanwi Mallick, Partha Pratim Das, and Arun Kumar Majumdar. 2019. Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach. arXiv preprint arXiv:1909.11023(2019).

[14]

Anupama Mallik and Santanu Chaudhury. 2012. Acquisition of multimedia ontology: an application in preservation of cultural heritage. International journal of multimedia information retrieval 1, 4(2012), 249–262.

[15]

Anupama Mallik, Santanu Chaudhury, and Hiranmay Ghosh. 2011. Nrityakosha: Preserving the intangible heritage of indian classical dance. Journal on Computing and Cultural Heritage (JOCCH) 4, 3 (2011), 1–25.

Digital Library

[16]

Anupama Mallik, Poornachander Pasumarthi, and Santanu Chaudhury. 2008. Multimedia ontology learning for automatic annotation and video browsing. In Proceedings of the 1st ACM international conference on Multimedia information retrieval. 387–394.

Digital Library

[17]

Vasileios Mezaris, Ioannis Kompatsiaris, Nikolaos V Boulgouris, and Michael G Strintzis. 2004. Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval. IEEE Transactions on Circuits and Systems for Video Technology 14, 5(2004), 606–621.

Digital Library

[18]

Nandikeśvara and Ananda Kentish Coomaraswamy. 1917. The mirror of gesture. Harvard University Press.

[19]

Milind R Naphade, T Kristjansson, Brendan Frey, and Thomas S Huang. 1998. Probabilistic multimedia objects (multijects): A novel approach to video indexing and retrieval in multimedia systems. In Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No. 98CB36269). IEEE, 536–540.

[20]

Katerina El Raheb, Theofilos Mailis, Vladislav Ryzhikov, Nicolas Papapetrou, and Yannis Ioannidis. 2017. Balonse: Temporal aspects of dance movement and its ontological representation. In European Semantic Web Conference. Springer, 49–64.

Digital Library

[21]

Balakrishnan Ramadoss and Kannan Rajkumar. 2007a. Semantic Modeling and Retrieval of Dance Video Annotations. INFOCOMP Journal of Computer Science 6, 1 (2007), 9–17.

[22]

Balakrishnan Ramadoss and Kannan Rajkumar. 2007b. Semi-automated annotation and retrieval of dance media objects. Cybernetics and Systems: An International Journal 38, 4 (2007), 349–379.

Digital Library

[23]

Anirban Saha, Saptami Ghosh, Partha Pratim Das, and Indranil Sarkar. 2021. Recognition and Classification of Accompanying Audios of Kathak Dance. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 860–864.

[24]

Sriparna Saha, Lidia Ghosh, Amit Konar, and Ramadoss Janarthanan. 2013. Fuzzy L membership function based hand gesture recognition for Bharatanatyam dance. In 2013 5th International Conference and Computational Intelligence and Communication Networks. IEEE, 331–335.

Digital Library

[25]

Anindhya Sankhla, Vinanti Kalangutkar, Himadri BGS Bhuyan, Tanwi Mallick, Vivek Nautiyal, Partha Pratim Das, and Arun Kumar Majumdar. 2017. Automated translation of human postures from kinect data to labanotation. In National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics. Springer, 494–505.

[26]

Simon Senecal, Niels A Nijdam, Andreas Aristidou, and Nadia Magnenat-Thalmann. 2020. Salsa dance learning evaluation and motion analysis in gamified virtual reality environment. Multimedia Tools and Applications 79, 33 (2020), 24621–24643.

Digital Library

[27]

M Thirumalai. 2001. An introduction to natya shastra-gesture in aesthetic arts. Language in India 1, 6 (2001), 27–33.

[28]

Christopher Welty. 2003. Ontology research. AI magazine 24, 3 (2003), 11–11.

Cited By

Paul SDas PRao K(2024)Ontology in Dance Domain—A SurveyJournal on Computing and Cultural Heritage 10.1145/369076718:1(1-32)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3690767
Jayanthi JMaheswari P(2024)AI and augmented reality for 3D Indian dance pose reconstruction cultural revivalScientific Reports10.1038/s41598-024-58680-w14:1Online publication date: 4-Apr-2024
https://doi.org/10.1038/s41598-024-58680-w

Index Terms

NrityaManch: An Annotation and Retrieval System for Bharatanatyam Dance
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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FIRE '22: Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation

December 2022

101 pages

ISBN:9798400700231

DOI:10.1145/3574318

Copyright © 2022 ACM.

© 2022 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 12 January 2023

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FIRE '22

FIRE '22: Forum for Information Retrieval Evaluation

December 9 - 13, 2022

Kolkata, India

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Cited By

Paul SDas PRao K(2024)Ontology in Dance Domain—A SurveyJournal on Computing and Cultural Heritage 10.1145/369076718:1(1-32)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3690767
Jayanthi JMaheswari P(2024)AI and augmented reality for 3D Indian dance pose reconstruction cultural revivalScientific Reports10.1038/s41598-024-58680-w14:1Online publication date: 4-Apr-2024
https://doi.org/10.1038/s41598-024-58680-w

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