Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Collaborative video reindexing via matrix factorization

Collaborative video reindexing via matrix factorization Collaborative Video Reindexing via Matrix Factorization MING-FANG WENG and YUNG-YU CHUANG, National Taiwan University Concept-based video indexing generates a matrix of scores predicting the possibilities of concepts occurring in video shots. Based on the idea of collaborative ltering, this article presents unsupervised methods to re ne the initial scores generated by concept classi ers by taking into account the concept-to-concept correlation and shot-to-shot similarity embedded within the score matrix. Given a noisy matrix, we re ne the inaccurate scores via matrix factorization. This method is further improved by learning multiple local models and incorporating contextual-temporal structures. Experiments on the TRECVID 2006 “2008 datasets demonstrate relative performance gains ranging from 13% to 52% without using any user annotations or external knowledge resources. Categories and Subject Descriptors: I.2.10 [Arti cial Intelligence]: Vision and Scene Understanding ”Video analysis General Terms: Algorithms, Experimentation. Additional Key Words and Phrases: Multimedia content analysis, semantic video indexing, concept detection, unsupervised learning, TRECVID ACM Reference Format: Weng, M.-F. and Chuang, Y.-Y. 2012. Collaborative video reindexing via matrix factorization. ACM Trans. Multimedia Comput. Commun. Appl. 8, 2, Article 23 (May 2012), 20 pages. DOI = 10.1145/2168996.2169003 http://doi.acm.org/10.1145/2168996.2169003 1. INTRODUCTION The advancement of content acquisition devices and data http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

Collaborative video reindexing via matrix factorization

Download PDF
20 pages

Loading next page...
 
/lp/association-for-computing-machinery/collaborative-video-reindexing-via-matrix-factorization-PH40OnkyBl
Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/2168996.2169003
Publisher site
See Article on Publisher Site

Abstract

Collaborative Video Reindexing via Matrix Factorization MING-FANG WENG and YUNG-YU CHUANG, National Taiwan University Concept-based video indexing generates a matrix of scores predicting the possibilities of concepts occurring in video shots. Based on the idea of collaborative ltering, this article presents unsupervised methods to re ne the initial scores generated by concept classi ers by taking into account the concept-to-concept correlation and shot-to-shot similarity embedded within the score matrix. Given a noisy matrix, we re ne the inaccurate scores via matrix factorization. This method is further improved by learning multiple local models and incorporating contextual-temporal structures. Experiments on the TRECVID 2006 “2008 datasets demonstrate relative performance gains ranging from 13% to 52% without using any user annotations or external knowledge resources. Categories and Subject Descriptors: I.2.10 [Arti cial Intelligence]: Vision and Scene Understanding ”Video analysis General Terms: Algorithms, Experimentation. Additional Key Words and Phrases: Multimedia content analysis, semantic video indexing, concept detection, unsupervised learning, TRECVID ACM Reference Format: Weng, M.-F. and Chuang, Y.-Y. 2012. Collaborative video reindexing via matrix factorization. ACM Trans. Multimedia Comput. Commun. Appl. 8, 2, Article 23 (May 2012), 20 pages. DOI = 10.1145/2168996.2169003 http://doi.acm.org/10.1145/2168996.2169003 1. INTRODUCTION The advancement of content acquisition devices and data

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: May 1, 2012

References

  • Semantic indexing of multimedia content using visual, audio, and text cues
    Adams W. H.