Real-time feature extraction from video stream data for stream ...

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Real-time feature extraction from video stream data for stream ...

List of Figures

5.6 The .m3u-playlist file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.7 Example for an error-response . . . . . . . . . . . . . . . . . . . . . . . . . 55

5.8 Invocation command for uncompressing a video file using xuggler . . . . . 56

5.9 Invocation command for uncompressing a video file using mencoder . . . . 56

5.10 Images resulting from decoding a video with xuggler . . . . . . . . . . . . 57

5.11 One capsule event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

6.1 The naming space for features. . . . . . . . . . . . . . . . . . . . . . . . . 60

6.2 The RGB channels of a color image . . . . . . . . . . . . . . . . . . . . . . 61

6.3 Image transformed to black and white with threshold. . . . . . . . . . . . 63

6.4 Image Smoothing Masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

6.5 Border Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

6.6 Difference Images with RapidMiner . . . . . . . . . . . . . . . . . . . . . . 65

6.7 Color Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

6.8 Multiple Color Image Opener . . . . . . . . . . . . . . . . . . . . . . . . . 67

6.9 The overall feature extraction process in RapidMiner . . . . . . . . . . . . 67

6.10 The AbstractImageProcessor . . . . . . . . . . . . . . . . . . . . . . 69

7.1 Streams framework process to detect shot boundaries . . . . . . . . . . . . 72

7.2 Examples for misclassified frames. . . . . . . . . . . . . . . . . . . . . . . 73

7.3 Decision tree to classify anchorshots vs. news report shots . . . . . . . . . 75

7.4 Two features with good differentiation power between anchorshots and

news report shots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

7.5 RapidMiner process for the feature selection using Naive Bayes. . . . . . . 77

7.6 Stream Framework process to detect anchorshots by applying a decision

tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

7.7 Inferring a model for anchorshot . . . . . . . . . . . . . . . . . . . . . . . 81

7.8 Shot length in news shows . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7.9 Average RGB values for capsule events . . . . . . . . . . . . . . . . . . . . 83

7.10 Stream Framework process to detect events in the caffee capsule video

stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

7.11 Average RGB values for different colored coffee capsules . . . . . . . . . . 85

7.12 Min RGB values for different colored coffee capsules . . . . . . . . . . . . 86

7.13 Coffee capsule color model creation . . . . . . . . . . . . . . . . . . . . . . 86

.1 Coffee capsules sorted in the order they were placed on the slide. . . . . . 106

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