CoChat
Sign Up
Cited by 10Digital and Traditional Archives ManagementPersonal Information Management and User BehaviorPhotography and Visual Culture

Investigating human behaviors in selecting personal photos to preserve memories

Ceroni, Andrea, Solachidis, Vassilis, Mingxin Fu, Kanhabua, Nattiya, Papadopoulou, Olga, Niederee, Claudia, Mezaris, Vasileios·2015·2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
DOI 10.1109/icmew.2015.7169750
Open this paper in CoChat — free account

Abstract

Photos are excellent means for keeping and refreshing memories. Digital photography, however, imposes new challenges for keeping photos accessible on the long run due to threats such as hard disk crashes, format changes, or storage medium decay. Safe long-term preservation, ensuring the longevity of photos, comes at a cost, suggesting a restriction of this investment to the most valuable photos. Therefore, understanding how people behave when selecting their most valuable photos from large collections is an important first step towards the development of automatic preservation approaches. In this paper, we conduct a user study of 35 participants in selecting personal photos for long-term preservation. The results of the study provide insights for the photo selection process. In addition, we propose a photo selection method based on machine learning and compared human selections with leading edge clustering techniques, highlighting significant issues in emulating human decision patterns.

Integrity signals

Registry

Crossref · 10.1109/icmew.2015.7169750

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 Ceroni et al., 2015

Cited by

Sorted by citations

Authors
Ceroni, A., Solachidis, V., Fu, M., Kanhabua, N., Papadopoulou, O., Niederee, C., & Mezaris, V.
Affiliations
  • Leibniz University Hannover (DE)
  • Information Technologies Institute (GR)
ORCID
Venue
2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Publisher
IEEE
Published
2015-06
Pages
1-6
DOI
10.1109/icmew.2015.7169750
OpenAlex
W2296129145
Cited by
10
References
0
Citations by year
2024: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 1 · 2015: 1
FWCI
16.90
Type
conference paper
Language
en
Open access
closed
Locations
IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (publishedVersion)
Funders
  • European Commission
  • FP7 Information and Communication Technologies
Topics
  • Social Sciences › Arts and Humanities › Conservation › Digital and Traditional Archives Management
  • Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
  • Social Sciences › Arts and Humanities › History › Photography and Visual Culture
Keywords
photoselection, personal photo collections, long-term digital preservation, memory preservation, digital photography, user study, clustering

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review