Abstract
The analysis of images for different purposes – particularly image clustering – has been the subject of several research streams in the past. Since the 1990s query by image content and, somewhat later, content-based image retrieval have been topics of growing scientific interest. A literature review shows that research on image analysis, so far, is primarily related to computer science. However, since the advent of Flickr and other media-sharing platforms there is an ever growing data base of images which reflects individual preferences regarding activities or interests. Hence, these data is promising to observe implicit preferences and complement classical efforts for several marketing purposes (see, e.g., Van House, Int J Hum-Comput Stud 67:1073–1086, 2009 or Baier D, Daniel I (2011) Image clustering for marketing purposes. In: W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme (eds) Challenges concerning the data analysis – Computer science – Optimization, vol. 43). Against this background, the present paper investigates options for clustering images on the basis of personal image preferences, e.g. to use the results for marketing purposes.
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Notes
- 1.
The last two numbers of the image ID have to be read as 01 = color and 02 = black and white image.
- 2.
Because analysis of the respective data set regarding the low-level features is still in progress in this paper only a few results are illustrated, namely those of the subset “city”.
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Acknowledgements
This research is supported by the BMBF-ForMaT-Projekt “Multimediale Aehnlichkeitssuche zum Matchen, Typologisieren und Segmentieren”.
Additionally I have to thank Sebastian Fruend, Bielefeld University, for auxiliary assistance in preparation and support of the online survey.
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Schindler, D. (2013). User-Generated Content for Image Clustering and Marketing Purposes. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_48
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