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An Introduction to Crowdsourcing for Language and Multimedia Technology Research
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Making a Scene: Alignment of Complete Sets of Clips Based on Pairwise Audio Match
"... As the amount of social video content captured at physicalworld events, and shared online, is rapidly increasing, there is a growing need for robust methods for organization and presentation of the captured content. In this work, we significantly extend prior work that examined automatic detection o ..."
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As the amount of social video content captured at physicalworld events, and shared online, is rapidly increasing, there is a growing need for robust methods for organization and presentation of the captured content. In this work, we significantly extend prior work that examined automatic detection of videos from events that were captured at the same time, i.e. “overlapping”. We go beyond finding pairwise matches between video clips and describe the construction of scenes, or sets of multiple overlapping videos, each scene presenting a coherent moment in the event. We test multiple strategies for scene construction, using a greedy algorithm to create a mapping of videos into scenes, and a clustering refinement step to increase the precision of each scene. We evaluate the strategies in multiple settings and show that a greedy and clustering approach results in best possible balance between recall and precision for all settings.
The MediaMill Search Engine Video
"... In this video demonstration, we advertise the MediaMill video search engine, a system that facilitates semantic access to video based on a large lexicon of visual concept detectors and interactive video browsers. With an ultimate aim to disseminate video retrieval research to a non-technical audienc ..."
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In this video demonstration, we advertise the MediaMill video search engine, a system that facilitates semantic access to video based on a large lexicon of visual concept detectors and interactive video browsers. With an ultimate aim to disseminate video retrieval research to a non-technical audience, we explain the need for a visual video retrieval solution, summarize the MediaMill technology, and hint at future perspectives.
CrowdsourcingVisualDetectorsforVideoSearch
"... In this paper, we study social tagging at the video fragmentlevel using a combination of automated content understanding and the wisdom of the crowds. We are interested in the question whether crowdsourcing can be beneficial to a video search engine that automatically recognizes video fragments on a ..."
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In this paper, we study social tagging at the video fragmentlevel using a combination of automated content understanding and the wisdom of the crowds. We are interested in the question whether crowdsourcing can be beneficial to a video search engine that automatically recognizes video fragments on a semantic level. To answer this question, we perform a 3-month online field study with a concert video search engine targeted at a dedicated user-community of pop concert enthusiasts. We harvest the feedback of more than 500 active users and perform two experiments. In experiment 1 we measure user incentive to provide feedback, in experiment 2 we determine the tradeoff between feedback quality and quantity when aggregated over multiple users. Results show that users provide sufficient feedback, which becomes highly reliable when a crowd agreement of 67 % is enforced.
CrowdsourcingVisualDetectorsforVideoSearch
"... In this paper, we study social tagging at the video fragmentlevel using a combination of automated content understanding and the wisdom of the crowds. We are interested in the question whether crowdsourcing can be beneficial to a video search engine that automatically recognizes video fragments on a ..."
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In this paper, we study social tagging at the video fragmentlevel using a combination of automated content understanding and the wisdom of the crowds. We are interested in the question whether crowdsourcing can be beneficial to a video search engine that automatically recognizes video fragments on a semantic level. To answer this question, we perform a 3-month online field study with a concert video search engine targeted at a dedicated user-community of pop concert enthusiasts. We harvest the feedback of more than 500 active users and perform two experiments. In experiment 1 we measure user incentive to provide feedback, in experiment 2 we determine the tradeoff between feedback quality and quantity when aggregated over multiple users. Results show that users provide sufficient feedback, which becomes highly reliable when a crowd agreement of 67 % is enforced.
A General-purpose Crowdsourcing Platform for Mobile Devices
"... Abstract: This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. ..."
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Abstract: This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and task-solver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform. 1
Socially-Aware Multimedia Authoring: Past, Present, and Future
"... Creating compelling multimedia productions is a nontrivial task. This is as true for creating professional content as it is for nonprofessional editors. During the past 20 years, authoring networked content has been a part of the research agenda of the multimedia community. Unfortunately, authoring ..."
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Creating compelling multimedia productions is a nontrivial task. This is as true for creating professional content as it is for nonprofessional editors. During the past 20 years, authoring networked content has been a part of the research agenda of the multimedia community. Unfortunately, authoring has been seen as an initial enterprise that occurs before ‘real ’ content processing takes place. This limits the options open to authors and to viewers of rich multimedia content for creating and receiving focused, highly personal media presentations. This article reflects on the history of multimedia authoring. We focus on the particular task of supporting socially-aware multimedia, in which the relationships within particular social groups among authors and viewers can be exploited to create highly personal media experiences. We provide an overview of the requirements and characteristics of socially-aware multimedia authoring within the context of exploiting community content. We continue with a short historical perspective on authoring support for these types of situations. We then present an overview of a current system for supporting socially-aware multimedia authoring within the community content. We conclude with a discussion of the issues that we feel can provide a fruitful basis for future multimedia authoring support. We argue that providing support for socially-aware multimedia authoring can have a profound impact on the nature and architecture of the entire multimedia
Scalable Crowd-Sourcing of Video from Mobile Devices
, 2012
"... We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture for this system is effectively a CDN in reverse. It achieves scalability by decentralizing the cloud computing infrastructure using VM-based cloudle ..."
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We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture for this system is effectively a CDN in reverse. It achieves scalability by decentralizing the cloud computing infrastructure using VM-based cloudlets. Based on time, location and content, privacy sensitive information is automatically removed from the video. This process, which we refer to as denaturing, is executed in a user-specific Virtual Machine (VM) on the cloudlet. Users can perform content-based searches on the total catalog of denatured videos. Our experiments reveal the bottlenecks for video upload, denaturing, indexing and content-based search and provide valuable insight on how parameters such as frame rate and resolution impact the system scalability.
Intel Labs
, 2012
"... We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture for this system is effectively a CDN in reverse. It achieves scalability by decentralizing the cloud computing infrastructure using VM-based cloudle ..."
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We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture for this system is effectively a CDN in reverse. It achieves scalability by decentralizing the cloud computing infrastructure using VM-based cloudlets. Based on time, location and content, privacy sensitive information is automatically removed from the video. This process, which we refer to as denaturing, is executed in a user-specific Virtual Machine (VM) on the cloudlet. Users can perform content-based searches on the total catalog of denatured videos. Our experiments reveal the bottlenecks for video upload, denaturing, indexing and content-based search and provide valuable insight on how parameters such as frame rate and resolution impact the system scalability.
