Context in Photo Albums: Understanding and Modeling User Behavior in Clustering and Selection
Abstract
Recent progress in digital photography and storage availability has drastically changed our approach to photo creation. While in the era of film cameras, careful forethought would usually precede the capture of a photo; nowadays, a large number of pictures can be taken with little effort. One of the consequences is the creation of numerous photos depicting the same moment in slightly different ways, which makes the process of organizing photos laborious for the photographer. Nevertheless, photo collection organization is important both for exploring photo albums and for simplifying the ultimate task of selecting the best photos. In this work, we conduct a user study to explore how users tend to organize or cluster similar photos in albums, to what extent different users agree in their clustering decisions, and to investigate how the clustering-defined photo context affects the subsequent photo-selection process. We also propose an automatic hierarchical clustering solution for modeling user clustering decisions. To demonstrate the usefulness of our approach, we apply it to the task of automatic photo evaluation within photo albums and propose a clustering-based context adaptation.
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Criteria that individuals find most important when manually selecting photos (such as personal relevance or a high probability to evoke memories) are precisely the criteria that are difficult to evaluate and predict for an algorithm.
“For instance, it has been observed that those criteria that individuals find most important when manually selecting photos (such as personal relevance or a high probability to evoke memories) are precisely the criteria that are difficult to evaluate and predict for an algorithm (Ceroni et al., 2015; see also Kuzovkin et al., 2019).”
From Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model (2024) · cites this paper as Kuzovkin et al., 2019
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Give and take: a study of consumer photo-sharing culture and practice
Miller, A. D., & Edwards, W. K. (2007) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240682 · Cited by 250 · see citing works
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Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model
Hutmacher, F., Appel, M., & Schwan, S. (2024) · Psychological Inquiry · doi:10.1080/1047840x.2024.2384125 · Cited by 12 · see citing works
- Authors
- Kuzovkin, D., Pouli, T., Meur, O. L., Cozot, R., Kervec, J., & Bouatouch, K.
- Affiliations
- Institut de Recherche en Informatique et Systèmes Aléatoires (FR)
- Technicolor (France) (FR)
- Centre National de la Recherche Scientifique (FR)
- Venue
- ACM Transactions on Applied Perception
- Published
- 2019-04-30
- Volume
- 16
- Issue
- 2
- Pages
- 1-20
- ISSN
- 1544-3558 (ISSN-L)
- 1544-3965
- OpenAlex
- W2969100931
- Cited by
- 10
- References
- 2
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 2 · 2019: 2
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Applied Perception (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Social Sciences › Arts and Humanities › History › Photography and Visual Culture
- Keywords
- photo albums, photo organization, image clustering, photoselection, hierarchical clustering, contextual adaptation, digital photography, photo collections
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