Name
Abstract | ||
|---|---|---|
We present a general approach to temporal media segmentation using supervised classification. Given standard low-level features representing each time sample, we build intermediate features via pairwise similarity. The intermediate features comprehensively characterize local temporal structure, and are input to an efficient supervised classifier to identify shot boundaries. We integrate discriminative feature selection based on mutual information to enhance performance and reduce processing requirements. Experimental results using large-scale test sets provided by the TRECVID evaluations for abrupt and gradual shot boundary detection are presented, demonstrating excellent performance |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TMM.2006.888015 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
feature extraction,image classification,image segmentation,learning (artificial intelligence),video coding,feature extraction,supervised classification,temporal media segmentation,temporal pattern classification,video segmentation,Video analysis,video shot boundary detection | Computer vision,Pattern recognition,Feature selection,TRECVID,Computer science,Segmentation,Feature extraction,Image segmentation,Artificial intelligence,Mutual information,Contextual image classification,Discriminative model | Journal |
Volume | Issue | ISSN |
9 | 3 | 1520-9210 |
Citations | PageRank | References |
31 | 1.21 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Cooper | 1 | 798 | 76.01 |
| Liu, T. | 2 | 31 | 1.21 |
| Eleanor Rieffel | 3 | 488 | 48.71 |