fifteen

In the world of image processing, a class is a set of patterns that share common properties and a pattern can be considered a set of features – color, shape and size for example.  These features can numerically be called feature vectors and can be used in pattern recognition to determine if a pattern belongs to a specific class. 

To perform pattern recognition, one has to define the features that will be able to separate classes from each other and then use these features to create a classifier.

In this activity, we use minimum distance classification to tell if objects belong in one class or another.  When you have a set of W classes, the mean feature vector of a class W_j is then defined as

where xj is the set of all feature vectors in the respective class and there are N_j samples in this class.  For the equation below, a sample must belong to a class if for an unknown vector x, it produces the smallest distance.

The following images were the different object classes and samples used in this activity.  The training set are presented in the top rows while the testing sets are shown in the bottom rows.

Figure 1. Class: 1-peso coin

Figure 2. Class: 25-cent coin

Figure 3. Class: Chiz Curlz

Figure 4. Class: Piattos

The classifier used in this activity was the Minimum Distance Classification system.  The overall accuracy rate is 60%.  However, the accuracy for the 1-peso coin class was 100%; the 25-cents coin class, 60%; the Chiz Curlz class, 20% and the Piattos class, 60%.  This may have something to do with the morphology of the samples in the objects themselves.  It’s funny to note that the four mistakenly classified samples had been identified as a 1-peso coin and, thrice, a Piattos chip. 

Overall grade: 9/10

REFERENCES

1. A14 – Pattern Recognition, Applied Physics 186 Manual