Large Scale Business Discovery from Street Level Imagery
Venue
arXiv (2015)
Publication Year
2015
Authors
Qian Yu, Christian Szegedy, Martin C. Stumpe, Liron Yatziv, Vinay Shet, Julian Ibarz, Sacha Arnoud
BibTeX
Abstract
Search with local intent is becoming increasingly useful due to the popularity of
the mobile device. The creation and maintenance of accurate listings of local
businesses worldwide is time consuming and expensive. In this paper, we propose an
approach to automatically discover businesses that are visible on street level
imagery. Precise business store-front detection enables accurate geo-location of
businesses, and further provides input for business categorization, listing
generation, etc. The large variety of business categories in different countries
makes this a very challenging problem. Moreover, manual annotation is prohibitive
due to the scale of this problem. We propose the use of a MultiBox [4] based
approach that takes input image pixels and directly outputs store front bounding
boxes. This end-to-end learning approach instead preempts the need for hand
modelling either the proposal generation phase or the post-processing phase,
leveraging large labelled training datasets. We demonstrate our approach
outperforms the state of the art detection techniques with a large margin in terms
of performance and run-time efficiency. In the evaluation, we show this approach
achieves human accuracy in the low-recall settings. We also provide an end-to-end
evaluation of business discovery in the real world.
