https://scholars.lib.ntu.edu.tw/handle/123456789/371495
標題: | Collaborative video reindexing via matrix factorization | 作者: | Weng, M.-F. YUNG-YU CHUANG |
關鍵字: | Concept detection; Multimedia content analysis; Semantic video indexing; TRECVID; Unsupervised learning | 公開日期: | 2012 | 卷: | 8 | 期: | 2 | 來源出版物: | ACM Transactions on Multimedia Computing, Communications and Applications | 摘要: | Concept-based video indexing generates a matrix of scores predicting the possibilities of concepts occurring in video shots. Based on the idea of collaborative filtering, this article presents unsupervised methods to refine the initial scores generated by concept classifiers by taking into account the concept-to-concept correlation and shot-to-shot similarity embedded within the score matrix. Given a noisy matrix, we refine 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. © 2012 ACM. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84861607626&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/371495 |
ISSN: | 15516857 | DOI: | 10.1145/2168996.2169003 | SDG/關鍵字: | Collaborative filtering; Concept detection; Concept-based; Data sets; External knowledge; Local model; Matrix factorizations; Multimedia content analysis; Noisy matrix; Re-indexing; Relative performance; Semantic video indexing; TRECVID; Unsupervised method; User annotations; Video indexing; Video shots; Indexing (of information); Semantics; Unsupervised learning; Refining |
顯示於: | 資訊工程學系 |
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