{"id":164845,"date":"2008-01-01T00:00:00","date_gmt":"2008-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-comprehensive-human-computation-framework-with-application-to-image-labeling\/"},"modified":"2018-10-16T21:03:57","modified_gmt":"2018-10-17T04:03:57","slug":"a-comprehensive-human-computation-framework-with-application-to-image-labeling","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-comprehensive-human-computation-framework-with-application-to-image-labeling\/","title":{"rendered":"A Comprehensive Human Computation Framework: With Application to Image Labeling"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Image and video labeling is important for computers to understand images and videos and for image and video search. Manual labeling is tedious and costly. Automatically image and video labeling is yet a dream. In this paper, we adopt a Web 2.0 approach to labeling images and videos efficiently: Internet users around the world are mobilized toapply their \u201ccommon sense\u201d to solve problems that are hard for today\u2019s computers, such as labeling images and videos. Wefirst propose a general human computation framework that binds problem providers, Web sites, and Internet users together to solve large-scale common sense problems efficiently and economically. The framework addresses the technical challenges such as preventing a malicious party from attacking others, removing answers from bots, and distilling human answers to produce high-quality solutions to the problems. The framework is then applied to labeling images. Three incremental refinement stages are applied. The first stage collects candidate labels of objects in animage. The second stage refines the candidate labels using multiple choices. Synonymic labels are also correlated in this stage. To prevent bots and lazy humans from selecting all the choices, trap labels are generated automatically and intermixed with the candidate labels. Semantic distance is used to ensure that the selected trap labels would be different enough from the candidate labels so that no human users would mistakenly select the trap labels. The last stage is to ask users to locate an object given a label from a segmented image. The experimental results are alsoreported in this paper. They indicate that our proposed schemes can successfully remove spurious answers from bots and distill human answers to produce high-quality image labels.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Image and video labeling is important for computers to understand images and videos and for image and video search. Manual labeling is tedious and costly. Automatically image and video labeling is yet a dream. In this paper, we adopt a Web 2.0 approach to labeling images and videos efficiently: Internet users around the world are 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Guo","user_id":0,"rest_url":false},{"type":"user_nicename","value":"linjuny","user_id":32691,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=linjuny"},{"type":"user_nicename","value":"spli","user_id":33697,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=spli"},{"type":"text","value":"Nenghai 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