  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.
