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

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

7. Experiments and Evaluation

The reason for choosing an evolutionary feature selection is quite easy. As mentioned

earlier, the idea was to extract as many features as possible by using RapidMiner IMMI.

Hence the feature selection is performed on a large feature set. As the search space for

a feature selection increases exponentially in the number of features (each feature can

either be chosen or not), it is almost impossible to try each combination. The exponential

feature selection operator tackles this problem.

Furthermore the evaluation of the selected features requires an inner X-validation. This

leads to an enormous computational complexity, as the learning algorithms have to be

trained and tested thousands of times. As training of an Support Vector Machine (SVM)

is way more complex then the training of a simple learner like Naive Bayes, I decided

to run the feature selection process, which is shown in figure 7.5, on Naive Bayes and

Decision Trees only. Training the SVM has unfortunately turned out to be impossible in

the available amount of time. Nevertheless, I expect these approaches to be a good first

measure for the importance of the extracted features.

In theory, an inner parameter optimization for the learner would be needed as well. Due

to the increasing computational complexity, I decided to rely on the default parameters

for each learner in this setting. A parameter optimization is performed in another

experiment, which is described later in this chapter.

Using Naive Bayes, ”frame:red:average” and ”frame:green:median” turned out to be the

best features, both being selected in 90% of all (ten) splits of the data, performed by

the outer X-validation. A full list of the top five ranked features is shown in table 7.5.

feature name


frame:red:average 0.9

frame:green:median 0.9

frame:red:median 0.8

frame:blue:median 0.8

frame:b a w:CenterOfMass:normalizedX 0.7

frame:blue:average 0.7

frame:green:average 0.7

frame:red:standardDeviation 0.7

Table 7.5.: Top features selected for Naive Bayes for distinguishing between anchorshots

and news report shots.

Of course the selected features do also strongly depend on the used learning algorithm.

Hence it suggests itself to run the experiment for different learning algorithms. Table 7.6

shows an overview of the top five ranked features for different learning algorithms. In

case more than one feature has the same weight than the fifth ranked feature, all equally

ranked features are listed.


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