INTELLIGENT CURATION OF DIGITAL PORTFOLIOS FOR ARTISTS

Authors

  • Shilpi Sarna Greater Noida, Uttar Pradesh 201306, India
  • Vimal Bibhu Professor, School of Engineering and Technology, Noida, International University, 203201, India
  • Mr. Debayan Das Assistant Professor, Department of Animation , Parul Institute of Design, Parul University, Vadodara, Gujarat, India
  • Yaduvir Singh Assistant Professor, Department of Computer Science and Engineering (AI), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Guntaj J Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Swetha Rajagopal Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
  • Anupama Abhijeet Deshpande Department of Engineering, Science and Humanities, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6825

Keywords:

Intelligent Curation, Digital Portfolios, Aesthetic Scoring, Art Recommendation Systems, Explainable AI, Cultural Informatics, Digital Exhibition Design

Abstract [English]

Intelligent curation represents a novel paradigm of digital art management where AI exists to supplement human creativity to make artistic portfolios more organized, interpreted, and personal. The current work describes a multimodal system consisting of computer vision, transformer-based language models, and reinforcement training that allows creating adaptive, explainable, and human aligning curation. The system evaluates works of art in terms of visual, semantic and emotional embedding to come up with aesthetic scores and curatorial suggestions which develop through the feedback of artists, curators and viewers. As proven by experimental case studies, such as solo views, multi-artist shows, and bespoke experiences of the viewer, AI can be used to improve the coherence of the curatorship, the range of subjects, and the experience of the viewer without losing interpretive integrity. The system has been assessed by precision, F1-score, aesthetic diverse metrics, and engagement index to indicate that the system is analytically robust. The conclusion of the paper is that human-AI co-curation is a contributor to a collaborative form of intelligence that builds upon the data analytics of curatorial reasoning into culture making and provides an ethically sound and scalable channel through which digital exhibition design can be carried out in the future.

References

Aslan, S., Castellano, G., Digeno, V., Migailo, G., Scaringi, R., and Vessio, G. (2022). Recognizing the Emotions Evoked by Artworks Through Visual Features and Knowledge Graph-Embeddings. In Image Analysis and Processing – ICIAP 2022 (Vol. 13373, pp. 129–140). Springer. https://doi.org/10.1007/978-3-031-13321-3_12

Bose, D., Somandepalli, K., Kundu, S., Lahiri, R., Gratch, J., and Narayanan, S. (2021). Understanding of Emotion Perception From art. Arxiv Preprint arXiv:2110.06486.

Dawson, A. (2020). Instagram Turns Ten: How the World’s Favourite Photo App Disrupted the Art Market. The Art Newspaper.

Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., and Goodman, S. (2019). Social Media Engagement Behavior: A Framework for Engaging Customers Through Social Media Content. European Journal of Marketing, 53, 2213–2243. https://doi.org/10.1108/EJM-03-2017-0182

González-Martín, C., Carrasco, M., and Wachter Wielandt, T. G. (2024). Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem. Empirical Studies of the Arts, 42, 38–64. https://doi.org/10.1177/02762374231163481

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.

Imran, S., Naqvi, R. A., Sajid, M., Malik, T. S., Ullah, S., Moqurrab, S. A., and Yon, D. K. (2023). Artistic Style Recognition: Combining Deep and Shallow Neural Networks for Painting Classification. Mathematics, 11, Article 4564. https://doi.org/10.3390/math11224564

Lee, S. G., and Cha, E. Y. (2016). Style Classification and Visualization of Art Painting’s Genre Using Self-Organizing Maps. Human-Centric Computing and Information Sciences, 6, Article 7. https://doi.org/10.1186/s13673-016-0063-4

McIntosh, M. J., and Morse, J. M. (2015). Situating and Constructing Diversity in Semi-Structured Interviews. Global Qualitative Nursing Research, 2, 1–12. https://doi.org/10.1177/2333393615597674

Medjani, J., Nadeau, J., and Rutter, R. (2019). Social Media Management, Objectification and Measurement in an Emerging Market. International Journal of Business and Emerging Markets, 11, 288–311. https://doi.org/10.1504/IJBEM.2019.102654

Moulard, J. G., Rice, D. H., Garrity, C. P., and Mangus, S. M. (2014). Artist Authenticity: How Artists’ Passion and Commitment Shape Consumers’ Perceptions and Behavioral Intentions Across Genders. Psychology and Marketing, 31, 576–590. https://doi.org/10.1002/mar.20719

Mughal, R., Polley, M., Sabey, A., and Chatterjee, H. J. (2022). How Arts, Heritage and Culture can Support Health and Wellbeing Through Social Prescribing. National Association of School Psychologists.

Serota, N. (2023). Introducing our strategy: Let’s create. Arts Council England.

Tashu, T. M., Hajiyeva, S., and Horvath, T. (2021). Multimodal Emotion Recognition from Art Using Sequential Co-Attention. Journal of Imaging, 7, Article 157. https://doi.org/10.3390/jimaging7080157

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Published

2025-12-25

How to Cite

Sarna, S., Bibhu, V., Das, D., Singh, Y., Guntaj J, Rajagopal, S., & Deshpande, A. A. (2025). INTELLIGENT CURATION OF DIGITAL PORTFOLIOS FOR ARTISTS. ShodhKosh: Journal of Visual and Performing Arts, 6(4s), 64–75. https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6825