A framework for robust feature selection for real-time fashion style recommendation

Authors:
Xiaofei Chao;Mark J. Huiskes;Tommaso Gritti;Calina Ciuhu
Affiliations:
Leiden University, Leiden, Netherlands;Leiden University, Leiden, Netherlands;Philips Research Laboratories, Eindhoven, Netherlands;Philips Research Laboratories, Eindhoven, Netherlands
Venue:
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Year:
2009

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Abstract

In this paper, we present the Smart Mirror system for fashion recommendation. The system uses intelligent vision technology to recognize clothing styles and supports real-time fashion recommendation. An important design challenge is to achieve sufficiently high style recognition accuracy while simultaneously offering robustness to input variations occurring in practice. We propose a framework for the selection of features that offer robust performance by assessing various evaluation measures under realistic deviations of optimal input data. The process is applied to a variety of low level features for clothing style description, including color histograms, local binary pattern (LBP) features and histogram of oriented gradient (HOG) features. We conclude the paper with an illustration of our results for web camera data and with a number of recommendations on how to move forward towards automatic fashion style perception.