Modern search engines receive large numbers of business related, local aware
queries. Such queries are best answered using accurate, up-to-date, business
listings, that contain representations of business categories. Creating such
listings is a challenging task as businesses often change hands or close down. For
businesses with street side locations one can leverage the abundance of street
level imagery, such as Google Street View, to automate the process. However, while
data is abundant, labeled data is not; the limiting factor is creation of large
scale labeled training data. In this work, we utilize an ontology of geographical
concepts to automatically propagate business category information and create a
large, multi label, training dataset for fine grained storefront classification.
Our learner, which is based on the GoogLeNet/Inception Deep Convolutional Network
architecture and classifies 208 categories, achieves human level accuracy.