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

ai.cs.uni.dortmund.de

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

7. Experiments and Evaluation

(a) red and green channel

(b) blue and green channel

Figure 7.12.: Min RGB values for different colored coffee capsules.

Inferring a model and evaluation

Based on all 72 events, included in the

two datasets, I now try to develop a model

by using different machine learning algorithms.

The RapidMiner process for inferring

and testing a model is shown in figure

7.13. It consists of two Read CVS operators,

which read in the data produced by

the streams framework. After joining the

data, it gets normalized to a range from

0 to 1. Afterwards it is splitting into ten

sets of training and testing data by using

a X-validation. The model is inferred

on nine of the data sets, whilst one set is

held back for testing. The parameters of

the learner are optimized by using an evolutionary

parameter optimization.

As the full data set only covers 72 examples

of coffee capsules events, the overall

process including X-validation and parameter

optimization can be run in a

reasonable amount of time for different

machine learning algorithms. The best

performance was obtained by a LibSVM

(C-SVC, linear kernel, C=6233.8, epsilon

0.0010), classifying the data with an accuracy

of 86.25 % ± 8.53 %. The resulting

confusion matrix is shown in figure 7.10.

Figure 7.13.: RapidMiner process for inferring

and testing a classifier on the extracted

dataset.

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