On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
VideoQA: question answering on news video
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Question answering on lecture videos: a multifaceted approach
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Photo-based question answering
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A syntactic tree matching approach to finding similar questions in community-based qa services
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Scalable image retrieval based on feature forest
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
From text question-answering to multimedia QA on web-scale media resources
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Exploring large scale data for multimedia QA: an initial study
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning cooking techniques from youtube
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Video reference: a video question answering engine
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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Community-based question answering systems have become very popular for providing answers to a wide variety of "how-to" questions. However most such systems present only textual answers. In many cases, users would prefer visual answers such as videos which are more direct and intuitive. Currently, there is very little research on automatically presenting precise reference videos based on user's question. In this paper, we explore how to leverage YouTube video collections as a source of reference to fulfilll such task and develop a novel multimedia application named:Video Reference. There are two steps to generating a video reference. The first is recall-driven video search, which is to increase the coverage of question by finding other similar questions. The second is precision-based video ranking. A three level ranking scheme based on visual analysis, opinion analysis and video redundancy is adopted to find the most relevant video reference from YouTube. Experiments conducted using questions from Consumer Electronics domain of Yahoo! Answers archive show the feasibility and effectiveness of our approach.