Wally

Abstract

In this paper we propose a crowd sourced approach for solving large scale object retrieval. We have built a tablet application which displays a query image and a database image. The crowd provides their input to indicate, if there is a match between the query and database image or not. We test our application on a crowd of low-income individuals. We observe that our target crowd had a very high accuracy on the considered dataset. We observe significant improvement as compared to vision based image matching algorithms available in prior-art. We also observe that with simplistic interfaces, even low literacy and low income people could participate in the crowdsourcing tasks. This provides them a significant income opportunity. We have validated our claims on two publicly available University of Kentucky datasets and ORL Face recognition dataset.