Image Feature Selection for Market Segmentation: A Comparison of Alternative Approaches

  • Susanne RumstadtEmail author
  • Daniel Baier
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The selection of variables (e.g. socio-demographic or psychographic descriptors of consumers, their buying intentions, buying frequencies, preferences) plays a decisive role in market segmentation. The inclusion as well as the exclusion of variables can influence the resulting classification decisively. Whereas this problem is always of importance it becomes overwhelming when customers should be grouped on the basis of describing images (e.g. photographs showing holidays experiences, usually bought products), as the number of potentially relevant image features is huge. In this paper we apply several general-purpose approaches to this problem: the heuristic variable selection by Carmone et al. (1999) and Brusco and Cradit (2001) as well as the model-based approach by Raftery and Dean (2004). We combine them with k-means, fuzzy c-means, and latent class analysis for comparisons in a Monte Carlo setting with an image database where the optimal market segmentation is already known.


Cluster Method Variable Selection Latent Class Analysis Color Histogram Market Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Marketing and Innovation Management, Institute of Business Administration and EconomicsBrandenburg University of Technology CottbusCottbusGermany

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