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

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

7. Experiments and Evaluation

Obviously, the median red value, the standard deviation of the blue values, and the

median blue value turn out to be features with good differentiation power between

anchorshots and news report shots. When inferring a larger and deeper decision tree,

further features like i.g. the normalized Y-coordinate of the center of mass of the red

color channel proof to be useful as well. The value of these features also get emphasized

when plotting the data. Figure 7.4 shows an example of such a plot. Obviously the

median blue value (Y-axis) and the normalized Y-coordinate of the center of mass of the

red color channel (X-axis) of anchorshots (within the given ”Tagesschau” dataset) are

always similar.

Figure 7.4.: The median blue value (Y-axis) and the normalized Y-coordinate of the

center of mass of the red color channel (X-axis) have turned out to be features with

good differentiation power between anchorshots (blue) and news report shots (red).

This figure shows the corresponding values for 5000 randomly draw examples for news

report shots and anchorshots from the ”Tagesschau” show of September 11th, 2012.

As the anchorshots are really similar and therefore overlaid each other, a small jitter

has been applied for a better illustration.

Beside decision trees many other learning algorithms can be used to determine a set of

features with good differentiation power. Support Vector Machines (SVM) assign weights

to each feature. The higher the assigned weight for one feature is, the better this feature

is to separate the classes. Hence I ran another experiment on the same data, using a

linear SVM to build a model for anchorshot classification. The top five ranked features

and their corresponding weights for two randomly drawn stratified samples of the data

are listed in table 7.4.

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