Visual Fashion-Product Search at SK Planet

Abstract

We build a large-scale visual search system which finds similar product images given a fashion item. Defining similarity among arbitrary fashion-products is still remains a challenging problem, even there is no exact ground-truth. To resolve this problem, we define more than 90 fashion-related attributes, and combination of these attributes can represent thousands of unique fashion-styles. The fashion-attributes are one of the ingredients to define semantic similarity among fashion-product images. To build our system at scale, these fashion-attributes are again used to build an inverted indexing scheme. In addition to these fashion-attributes for semantic similarity, we extract colour and appearance features in a region-of-interest (ROI) of a fashion item for visual similarity. By sharing our approach, we expect active discussion on that how to apply current computer vision research into the e-commerce industry.

13 pages, 11 figures, 3 tables

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
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• Recurrent Neural Network Regularization
• Microsoft COCO Captions: Data Collection and Evaluation Server
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Cited by in corpus (2)

• Visual Search at eBay
• One-Shot Item Search with Multimodal Data