Activity 14: Pattern recognition

In this activity, we are to classify objects to classes using the Euclidian mean approximation. A training set is used to extract certain features present in all the classes and is distinct within each class. The mean feature of the training set is used to classify objects of unknown class. The objects I used for this activity are beads from a rosary and 25-centavo coins. These are shown below.

From this set of objects, 5 coins and 5 beads were used as a training set. To extract the features, I used parametric segmentation used in activity 12. The features extracted are the average red (R), green (G), and blue (B) color in the image. Also extracted was the area obtained from the segmented image by first inverting the image using GIMP and then binarizing it in Scilab. The resulting features and the mean features are shown in the table below.

The remaining 5 images of each class were used as the test set. The same processes were done to extract the features of test objects. Afterwhich, the features from each objects were subtracted from the mean feature obtained in the training set. Specifically,

where Dj is the distance of one feature of the test object x from the mean feature mj of each class.

An object is classified as the object in a class where Dj is minimum. The classification in this case was done in Excel. The results are as follows:
Note that 1 = bead and 2 = coin for the classification. From the tables, it can be readily seen that a 100% classification is obtained by examining the area and the blue color present in the images. For the green and red colors, poor classifcation was obtained. This is because the obtained red and green features from the test objects have almost nearvalues. We can conclude that classification by using the Euclidian mean is effective only to a certain extent.

I give myself an 8/10 for this activity for poor classification from the red and green colors and for doing the acivity alone. ^_^