Publication Details
Title: There is No Data Like Less Data: Percepts for Video Concept Detection on Consumer-Produced Media
Author: B. Elizalde, G. Friedland, H. Lei, and A. Divakaran
Bibliographic Information: Proceedings of the ACM International Workshop on Audio and Multimedia Methods for Large-Scale Video Analysis (AMVA) at ACM Multimedia 2012 (MM'12), Nara, Japan, pp. 27-32
Date: October 2012
Research Area: Audio and Multimedia
Type: Article in conference proceedings
PDF: https://www.icsi.berkeley.edu/pubs/speech/nodatalikelessdata12.pdf
Overview:
Video concept detection aims to find videos that show a certain event described as a high-level concept, e.g. “wedding ceremony" or “changing a tire". This paper presents a theoretical framework and experimental evidence suggesting that video concept detection on consumer-produced videos can be performed by what we call “percepts", which is a set of observable units with Zipfian distribution. We present an unsupervised approach to extract percepts from audio tracks, which we then use to perform experiments to provide evidence for the validity of the proposed theoretical framework using the TRECVID MED 2011 dataset. The approach suggests selecting the most relevant percepts for each concept automatically, thereby actually filtering, selecting and reducing the amount of training data needed. It is show that our framework provides a highly usable foundation for doing video retrieval on consumer- produced content and is applicable for acoustic, visual, as well as multimodal content analysis.
Acknowledgements:
Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20066. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusion contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsement, either expressed or implied, of IARPA, DOI/NBC, or the U.S. Government.
Bibliographic Reference:
B. Elizalde, G. Friedland, H. Lei, and A. Divakaran. There is No Data Like Less Data: Percepts for Video Concept Detection on Consumer-Produced Media. Proceedings of the ACM International Workshop on Audio and Multimedia Methods for Large-Scale Video Analysis (AMVA) at ACM Multimedia 2012 (MM'12), Nara, Japan, pp. 27-32, October 2012
Author: B. Elizalde, G. Friedland, H. Lei, and A. Divakaran
Bibliographic Information: Proceedings of the ACM International Workshop on Audio and Multimedia Methods for Large-Scale Video Analysis (AMVA) at ACM Multimedia 2012 (MM'12), Nara, Japan, pp. 27-32
Date: October 2012
Research Area: Audio and Multimedia
Type: Article in conference proceedings
PDF: https://www.icsi.berkeley.edu/pubs/speech/nodatalikelessdata12.pdf
Overview:
Video concept detection aims to find videos that show a certain event described as a high-level concept, e.g. “wedding ceremony" or “changing a tire". This paper presents a theoretical framework and experimental evidence suggesting that video concept detection on consumer-produced videos can be performed by what we call “percepts", which is a set of observable units with Zipfian distribution. We present an unsupervised approach to extract percepts from audio tracks, which we then use to perform experiments to provide evidence for the validity of the proposed theoretical framework using the TRECVID MED 2011 dataset. The approach suggests selecting the most relevant percepts for each concept automatically, thereby actually filtering, selecting and reducing the amount of training data needed. It is show that our framework provides a highly usable foundation for doing video retrieval on consumer- produced content and is applicable for acoustic, visual, as well as multimodal content analysis.
Acknowledgements:
Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20066. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusion contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsement, either expressed or implied, of IARPA, DOI/NBC, or the U.S. Government.
Bibliographic Reference:
B. Elizalde, G. Friedland, H. Lei, and A. Divakaran. There is No Data Like Less Data: Percepts for Video Concept Detection on Consumer-Produced Media. Proceedings of the ACM International Workshop on Audio and Multimedia Methods for Large-Scale Video Analysis (AMVA) at ACM Multimedia 2012 (MM'12), Nara, Japan, pp. 27-32, October 2012