Ontological Supervision for Fine Grained Classification of Street View Storefronts
Abstract
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.
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
