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

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

7.2. Segmentation and Tagging of News shows

Learning Algorithm

Selected Features (weights)

Naive Bayes 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)

Decision Tree frame:red:median (1.0), frame:blue:median (0.9)

frame:red:standardDeviation (0.9)

frame:grey:CenterOfMass:normalizedX (0.8)

frame:blue:average (0.7)

frame:blue:CenterOfMass:normalizedX (0.7)

frame:blue:CenterOfMass:normalizedY (0.7)

frame:green:CenterOfMass:normalizedX (0.7)

frame:red:CenterOfMass:normalizedY (0.7)

Table 7.6.: Top features selected by different learning algorithms for distinguishing between

anchorshots and news report shots.

Beside a ranking of the features, the experiment also provides us with the accuracy of

the used learning algorithms with regard to the learning task of detection anchorshots.

The resulting accuracies are as follows: Naive Bayes scored 98.87% ± 0.19%. Decision

Trees had a perfect performance of 100%.

7.2.2. Real-time model-based anchorshot detection

In chapter 2.3.3 I have presented three types of anchorshot detection approaches: model

matching, face detection, and frame similarity. As mentioned, the frame similarity is

an unsupervised approach clustering all shot and taking the most dense cluster as the

anchorshot cluster. As said before, it is thus not easily transferable to stream data.

Moreover, relying on face detection is only reasonable if position and size of the face

are also taken into account. Hence the most promising and probably easiest approach

for anchorshot detection on video streams is matching each shot against a model, which

describes an anchorshot.

Applying a Decision Tree-Model

In the previous section 7.2.1, I have used RapidMiner to infer a decision tree that allows

as to classify shots into anchorshots and news report shot. Although this model

was learned offline, we can of course apply it online. This can be done by using the

ApplyDecisionTreeModel processor. The corresponding process is shown in figure 7.6.


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