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

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

7.2. Segmentation and Tagging of News shows

7.2. Segmentation and Tagging of News shows

7.2.1. Feature-based anchorshot detection using RapidMiner

As mentioned before, I decided to first of all extract a great amount of different features

from the news show dataset by using RapidMiner and its Image Mining Extension. A

full list of all extracted features can be found in the Appendix. In order to figure out,

which of these features are useful, I trained a decision tree learner on the labeled dataset

of three news shows. As described in chapter (see 3.2.2), decision tree learners work topdown

by splitting the examples at each step using a feature that splits the example set

best. Hence all features, that appear on one of the first levels of the inferred decision tree,

have a high power to distinguish between the classes. For this experiment, I decided to

focus on distinguishing between anchorshots and news report shots only. Thus the input

for the learner consists out of a subset of 58.987 examples derives from the ”Tagesschau”

shows of September 13th and 15th, and October 27th 2012. The example set includes

14.249 anchorshots and 44.738 news report shots. The inferred decision tree is shown in

figure 7.3.

frame:red:median

> 62.5

≤ 62.5

Report

frame:blue:standardDeviation

> 38.823 ≤ 38.823

frame:blue:median

Report

> 102.5 ≤ 102.5

Anchorshot

Report

Figure 7.3.: Decision tree for the classification of anchorshots. The tree was inferred

using all news report shots and anchorshots from three ”Tagesschau” shows (Sep. 13th

and 15th, and Oct. 27th 2012). Parameters for Decision Tree operator: {criterion =

gain ration, minimal size for split = 4, minimal leaf size = 2, minimal gain = 0.1,

maximal depth = 4, confidence = 0.25, prepruning alternatives = 3}.

75

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