Abstract
In this paper, we evaluate managing huge sets of visual content as we know handling large image collections has always been a challenge for technologists. With the rise of various social media platforms that aggregate a vast amount visual data it causes new implications not only for the computer scientists but also for curators, designers, social scientists, statisticians and end users: how to navigate the data efficiently, access to new levels of exploration and visualize the content without too many details being lost. With more and more such platforms, the bigger this challenge is going to get. In 22 semi structured surveys in Oslo (Norway), we find that a set of factors intermix to inform perceptions about large image collections - including how often they look-over large image collections.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barthel, K., Hezel, N.: Graph navigation for exploring very large image collections. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 411–416 (2017)
Heesch, D.: A survey of browsing models for content-based image retrieval. Multimedia Tools and Appl. Arch. 40(2), 261–284, November (2008)
Pečenovic, Z., Do, M.N., Vetterli, M., Pu, P.: Integrated browsing and searching of large image collections, pp. 279–289. In Advances in Visual Information Systems. Springer, Berlin Heidelberg (2000)
Jing, Y., et al.: Google image swirl: a large-scale content-based image visualization system. Proceedings of WWW, pp. 539–540 (2012)
Strong, G., Hoque, E., Gong, M., Hoeber, O.: Organizing and browsing image search results based on conceptual and visual similarities. In: ISVC, pp. 481–490 (2010)
Wang, J., Jia, L., Hua, X.-S.: Interactive browsing via diversified visual summarization for image search results. Multimedia Syst. 17(5), 379–391 (2011)
Yee, K.P., Swearingen, K., Li, K., Hearst, M.: Faceted metadata for image search and browsing. In: Proc CHI 2003, pp. 401–408. ACM Press (2003)
Barthel, K., Hezel, N.: Graph navigation for exploring very large image collections. International Conference on Computer Vision Theory and Applications (2017)
André, P., Cutrell, E., Tan, D.S., Smith, G.: Designing novel image search interfaces by understanding unique characteristics and usage. INTERACT ’09, pp. 340–353 (2009)
Chew, B., Rode, J.A., Sellen, A.: Understanding the everyday use of images on the web. In: Proc. NordiCHI’10, pp. 102–111. ACM Press (2010)
Markkula, M., Sormunen, E.: End-user searching challenges indexing practices in the digital newspaper photo archive. Inf. Retrieval 1(4), 259–285 (2000)
Chung, E., Yoon, J.: Image needs in the context of image use: an exploratory study. J. Inf. Sci. (2011)
Shatford-Layne, S.: Some issues in the indexing of images. J. Am. Soc. Inf. Sci. 45(8), 583–588 (1994)
Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: Proc. of SIGMM, pp. 253–262. ACM Press (2005)
Barthel, K., et al.: ImageMap - visually browsing millions of images. Multimedia Modeling; 21st International Conference (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Goethe, O. (2020). From Aggregation to Navigation: Large Image Collections Seeking and Exploration. In: Ahram, T., Taiar, R., Colson, S., Choplin, A. (eds) Human Interaction and Emerging Technologies. IHIET 2019. Advances in Intelligent Systems and Computing, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-030-25629-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-25629-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25628-9
Online ISBN: 978-3-030-25629-6
eBook Packages: EngineeringEngineering (R0)