Ontological Supervision for Fine Grained Classification of Street View Storefronts

Qian Yu
Martin C. Stumpe
Vinay Shet
Sacha Arnoud
Liron Yatziv


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.

Research Areas