  Fashion is an increasingly important topic in computer vision, in particular
the so-called street-to-shop task of matching street images with shop images
containing similar fashion items. Solving this problem promises new means of
making fashion searchable and helping shoppers find the articles they are
looking for. This paper focuses on finding pieces of clothing worn by a person
in full-body or half-body images with neutral backgrounds. Such images are
ubiquitous on the web and in fashion blogs, and are typically studio photos, we
refer to this setting as studio-to-shop. Recent advances in computational
fashion include the development of domain-specific numerical representations.
Our model Studio2Shop builds on top of such representations and uses a deep
convolutional network trained to match a query image to the numerical feature
vectors of all the articles annotated in this image. Top-$k$ retrieval
evaluation on test query images shows that the correct items are most often
found within a range that is sufficiently small for building realistic visual
search engines for the studio-to-shop setting.
