Skip to main content
SpringerLink
Log in

Unsupervised learning on multimedia data: a Cultural Heritage case study

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Integrating and analyzing a large amount of data extracted from different sources can be considered a key asset for businesses, organizations, research institutions that also deal with the Cultural Heritage domain. In the last decade, Internet of Things (IoT) technologies and the massive use of mobile devices contributed to generate an enormous flow of multimedia data, whose collection, analysis and interpretation allows for real-time analysis related to the behaviours, preferences and opinions of users. In this paper we present and discuss an unsupervised learning approach on multimedia features of a dataset coming from an Internet of Things framework. The main research objective of this work is to assess how the collection of behavioural IoT data coming from the Cultural Heritage domain can be opportunely exploited by means of unsupervised learning techniques in order to produce useful insights for the stakeholders, especially considering the multimedia features of such data. The presented experimental results, executed in a real case study, assess how the Cultural Heritage domain, and the related stakeholders, can benefit from these kind of services and applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Aria M, Cuccurullo C (2017) Bibliometrix: an r-tool for comprehensive science mapping analysis. J Inform 11(4):959–975. https://doi.org/10.1016/j.joi.2017.08.007

    Article  Google Scholar 

  2. Bollo A (2016) Il monitoraggio e la valutazione dei pubblici dei musei. gli osservatori dei musei nell’esperienza internazionale. Quaderni della Valorizzazione – Nuova Serie, 2, Direzione Generale Valorizzazione-Mibact

  3. Ibba A, Duin R, Lee WJ (2010) A study on combining sets of differently measured dissimilarities. pp 3360–3363. https://doi.org/10.1109/ICPR.2010.820

  4. Jung YJ, Zimmerman HT, Pérez-Edgar K (2018) A methodological case study with mobile eye-tracking of child interaction in a science museum. TechTrends 62(5):509–517. https://doi.org/10.1007/s11528-018-0310-9

    Article  Google Scholar 

  5. Katifori A, Karvounis M, Kourtis V, Perry S, Roussou M, Ioanidis Y (2018) Applying interactive storytelling in cultural heritage: opportunities, challenges and lessons learned. In: Rouse R, Koenitz H, Haahr M (eds) Interactive storytelling. Springer International Publishing, Cham, pp 603–612

  6. Kaufman L, Rousseeuw PJ (1987) Clustering by means of medoids

  7. Krause A, Siewiorek DP, Smailagic A, Farringdon J (2003) Unsupervised, dynamic identification of physiological and activity context in wearable computing. In: Proceedings of the 7th IEEE international symposium on wearable computers (ISWC’03). IEEE Computer Society, Washington, p 88

  8. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2019) Cluster: cluster analysis basics and extensions. R package version 2.1.0 — for new features, see the ‘Changelog’ file (in the package source)

  9. Martella C, Miraglia A, Frost J, Cattani M, van Steen M (2017) Visualizing, clustering, and predicting the behavior of museum visitors. Pervasive Mobile Comput 38:430–443. https://doi.org/10.1016/j.pmcj.2016.08.011

    Article  Google Scholar 

  10. Ohlei A, Bouck-Standen D, Winkler T, Herczeg M (2018) Infogrid: acceptance and usability of augmented reality for mobiles in real museum contexts. In: Dachselt R, Weber G (eds) Mensch und computer 2018 - workshopband. Gesellschaft für Informatik e.V., Bonn

  11. Pekalska E, Duin R (2001) On combining dissimilarity representations. Multiple Classifier Systems: 359–368

  12. Piccialli F, Yoshimura Y, Benedusi P, Ratti C, Cuomo S (2019) Lessons learned from longitudinal modeling of mobile-equipped visitors in a complex museum. Neural Comput Appl: 1–17

  13. Pierdicca R, Marques-Pita M, Paolanti M, Malinverni ES (2019) Iot and engagement in the ubiquitous museum. Sensors 19(6):1387. https://doi.org/10.3390/s19061387. https://www.mdpi.com/1424-8220/19/6/1387

    Article  Google Scholar 

  14. Portalés C, Rodrigues JMF, Rodrigues Gonçalves A, Alba E, Sebastián J (2018) Digital cultural heritage. Multimodal Technologies and Interaction 2(3), https://doi.org/10.3390/mti2030058. https://www.mdpi.com/2414-4088/2/3/58

  15. Reynolds AP, Richards G, de la Iglesia B, Rayward-Smith VJ (2006) Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J Math Modelling Algorithms 5(4):475–504. https://doi.org/10.1007/s10852-005-9022-1

    Article  MathSciNet  MATH  Google Scholar 

  16. Roussou M, Katifori A (2018) Flow, staging, wayfinding, personalization: evaluating user experience with mobile museum narratives. Multimodal Technologies and Interaction 2(2):32. https://doi.org/10.3390/mti2020032

    Article  Google Scholar 

  17. Traboulsi C, Frau M, Cabiddu F (2018) Active seniors perceived value within digital museum transformation. The TQM Journal 30 (5):530–553. https://doi.org/10.1108/tqm-11-2017-0155

    Article  Google Scholar 

  18. MA W (1935) Problems of installation in museums of art. American Association of Museums 14:29–30

    Google Scholar 

  19. Won H, Yu J (2018) Interactive media art based on location and motion tracking of multi-performers. In: Ioannides M, Fink E, Brumana R, Patias P, Doulamis A, Martins J, Wallace M (eds) Digital heritage. Progress in cultural heritage: documentation, preservation, and protection. Springer International Publishing, Cham, pp 111–117

  20. Zancanaro M, Kuflik T, Boger Z, Goren-Bar D, Goldwasser D (2007) Analyzing museum visitors’ behavior patterns. In: Conati C, McCoy K, Paliouras G (eds) User modeling 2007. Springer Berlin Heidelberg, Berlin, pp 238–246

Download references

Acknowledgements

This work was supported by the OPS-REMIAM project [Grant Number PON03PE_00161] and the Cultural Equipment with Transmedial Recommendation Analytics - C.E.T.R.A. project [Regione Campania - Bando RIS3 2018 - Fase 2 - Supporto di progetti, anche collaborativi, di sviluppo precompetitivo, trasferimento tecnologico da parte delle MPMI campane]

Author information

Authors and Affiliations

  1. University of Naples Federico II, Naples, Italy

    Francesco Piccialli, Giampaolo Casolla, Salvatore Cuomo, Fabio Giampaolo, Edoardo Prezioso & Vincenzo Schiano di Cola

Authors
  1. Francesco Piccialli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giampaolo Casolla
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salvatore Cuomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabio Giampaolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edoardo Prezioso
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vincenzo Schiano di Cola
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Piccialli.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Piccialli, F., Casolla, G., Cuomo, S. et al. Unsupervised learning on multimedia data: a Cultural Heritage case study. Multimed Tools Appl 79, 34429–34442 (2020). https://doi.org/10.1007/s11042-020-08781-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08781-1

Keywords

Search

Navigation