CrowdSearch
- 15 June 2010
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
Mobile phones are becoming increasingly sophisticated with a rich set of on-board sensors and ubiquitous wireless connectivity. However, the ability to fully exploit the sensing capabilities on mobile phones is stymied by limitations in multimedia processing techniques. For example, search using cellphone images often encounters high error rate due to low image quality. In this paper, we present CrowdSearch, an accurate image search system for mobile phones. CrowdSearch combines automated image search with real-time human validation of search results. Automated image search is performed using a combination of local processing on mobile phones and backend processing on remote servers. Human validation is performed using Amazon Mechanical Turk, where tens of thousands of people are actively working on simple tasks for monetary rewards. Image search with human validation presents a complex set of tradeoffs involving energy, delay, accuracy, and monetary cost. CrowdSearch addresses these challenges using a novel predictive algorithm that determines which results need to be validated, and when and how to validate them. CrowdSearch is implemented on Apple iPhones and Linux servers. We show that CrowdSearch achieves over 95% precision across multiple image categories, provides responses within minutes, and costs only a few cents.Keywords
This publication has 15 references indexed in Scilit:
- MAUIPublished by Association for Computing Machinery (ACM) ,2010
- SurroundSensePublished by Association for Computing Machinery (ACM) ,2009
- Distributed image search in camera sensor networksPublished by Association for Computing Machinery (ACM) ,2008
- reCAPTCHA: Human-Based Character Recognition via Web Security MeasuresScience, 2008
- Get another label? improving data quality and data mining using multiple, noisy labelersPublished by Association for Computing Machinery (ACM) ,2008
- Crowdsourcing user studies with Mechanical TurkPublished by Association for Computing Machinery (ACM) ,2008
- Object retrieval with large vocabularies and fast spatial matchingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- People-centric urban sensingPublished by Association for Computing Machinery (ACM) ,2006
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Labeling images with a computer gamePublished by Association for Computing Machinery (ACM) ,2004