The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Human computation
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Face Recognition Using Scale Invariant Feature Transform and Support Vector Machine
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
txteagle: Mobile Crowdsourcing
IDGD '09 Proceedings of the 3rd International Conference on Internationalization, Design and Global Development: Held as Part of HCI International 2009
Quality management on Amazon Mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
TurKit: human computation algorithms on mechanical turk
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
VizWiz: nearly real-time answers to visual questions
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Evaluating and improving the usability of Mechanical Turk for low-income workers in India
Proceedings of the First ACM Symposium on Computing for Development
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
CrowdForge: crowdsourcing complex work
Proceedings of the 24th annual ACM symposium on User interface software and technology
mClerk: enabling mobile crowdsourcing in developing regions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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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.