Aesthetic quality inference for online fashion shopping

Ming Chen; Jan Allebach

doi:10.1117/12.2045269

3 March 2014 Aesthetic quality inference for online fashion shopping

Ming Chen, Jan Allebach

Proceedings Volume 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014; 902703 (2014) https://doi.org/10.1117/12.2045269
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

On-line fashion communities in which participants post photos of personal fashion items for viewing and possible purchase by others are becoming increasingly popular. Generally, these photos are taken by individuals who have no training in photography with low-cost mobile phone cameras. It is desired that photos of the products have high aesthetic quality to improve the users’ online shopping experience. In this work, we design features for aesthetic quality inference in the context of online fashion shopping. Psychophysical experiments are conducted to construct a database of the photos’ aesthetic evaluation, specifically for photos from an online fashion shopping website. We then extract both generic low-level features and high-level image attributes to represent the aesthetic quality. Using a support vector machine framework, we train a predictor of the aesthetic quality rating based on the feature vector. Experimental results validate the efficacy of our approach. Metadata such as the product type are also used to further improve the result.

Citation Download Citation

Ming Chen and Jan Allebach "Aesthetic quality inference for online fashion shopping", Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 902703 (3 March 2014); https://doi.org/10.1117/12.2045269

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