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Proceedings Paper

Aesthetic quality inference for online fashion shopping
Author(s): Ming Chen; Jan Allebach
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Paper 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.

Paper Details

Date Published: 3 March 2014
PDF: 7 pages
Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 902703 (3 March 2014); doi: 10.1117/12.2045269
Show Author Affiliations
Ming Chen, Purdue Univ. (United States)
Jan Allebach, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 9027:
Imaging and Multimedia Analytics in a Web and Mobile World 2014
Qian Lin; Jan Philip Allebach; Zhigang Fan, Editor(s)

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