Cited by 10Image Retrieval and Classification TechniquesAdvanced Clustering Algorithms ResearchPhotography and Visual Culture

Context in Photo Albums: Understanding and Modeling User Behavior in Clustering and Selection

Kuzovkin, Dmitry, Pouli, Tania, Meur, Olivier Le, Cozot, Rémi, Kervec, Jonathan, Bouatouch, Kadi·2019·ACM Transactions on Applied Perception

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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Crossref · 10.1145/3333612

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2 of 2 references verified0 citation errors · 0 not checkable

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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

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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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