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# Classification

[Laboratory]

In this experiment we use the same images used in the Feature Extraction lesson.

Original binary image

Labeled image

The first invariant moment is extracted and plotted below. The background was made object 0 with feature value set to -1.

Region number by first invariant moment

Picking one sample of each object and assigning a class to it:

     class   object           object number    feature value
     ---------------------------------------------------------
       0     background           0              -1.00
       1     nail                 1               0.93
       2     ring screw           2               0.30
       3     tee-pin              5               1.92
       4     small dot noise     18               0.00

The Minimum Distance Classify operator assigns to each object the closest class according to the feature distance. The distance metric commonly used is Euclidean distance.

The result of the classify is a table with the relationship of objects and its class. Using the same technique as in the Area Measurement and Display lesson, we can assign a class value to the pixels of each object. This enables to visualize the result of the classification.

Classified image

It can be noted that there were 2 misclassifications corresponding to two tee-pin objects

     object feature value
     --------------------
       3       1.01
      12       1.41

This is consistent with the nearest distance classify method because these feature values are closer to the attribute of the nail (0.93) than of the tee-pin (1.92). There are several ways to solve this: by choosing a better sample value, or by choosing an additional feature value, etc.

- Lab: Classification