Name
Abstract | ||
|---|---|---|
This paper presents a method to automatically detect slide changes in lecture videos. For accurate detection, the regions capturing slide images are first identified from video frames. Then, SIFT features are extracted from the regions, which are invariant to image scaling and rotation. These features are used to compare similarity between frames. If the similarity is smaller than a threshold, slide transition is detected. The threshold is estimated based on the mean and standard deviation of sample frames' similarities. Using this method, high detection accuracy can be obtained without any supplementary slide images. The proposed method also supports detection of backward slide transitions that occur when a speaker returns to a previous slide to emphasize its contents. In experiments conducted on our test collection, the proposed method showed 87 % accuracy in forward transition detection and 86 % accuracy in backward transition detection. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11042-014-1990-6 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Video segmentation,Video indexing,Recursive interval pruning,Backward slide transitions,Adaptive threshold,SIFT algorithm | Computer vision,Scale-invariant feature transform,Pattern recognition,Computer science,Invariant (mathematics),Artificial intelligence,Standard deviation,Image scaling | Journal |
Volume | Issue | ISSN |
74 | 18 | 1380-7501 |
Citations | PageRank | References |
2 | 0.36 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun Ji Jeong | 1 | 7 | 0.83 |
| Tak-Eun Kim | 2 | 51 | 1.87 |
| Hyeon Gyu Kim | 3 | 14 | 5.03 |
| Myoung Ho Kim | 4 | 1040 | 273.40 |