Activity18: Pattern recognition

Pattern recognition under the domain of machine learning. It has many applications in medicine, gait analysis, speech recognition and text classification. Pattern recognition technique is done through feature extraction and then classification of the gathered features. The test set will then be subjected for comparison based from the features obtained from the training set.

In this activity, we use different objects belonging to different categories which are to be extracted from the data. Below are samples of object images used for pattern recognition technique.

Pillow


Piatos

Fishball

Kwek kwek

For each image, I used R,G,B values, shape and area as classifiers. Each feature is normalized such that the bias to large values or very small values will be removed. 4 samples of each set is used as training set and the other 4 is used as test set.

The table below shows the percent of success of recognition based from the chosen features.

	Pillow	Piatos	Fishball	Kwek kwek
Pillow	4/4	1/4	0/4	0/4
Piatos	1/4	3/4	0/4	0/4
Fishball	0/4	1/4	3/4	2/4
Kwek kwek	0/4	1/4	1/4	4/4

Based from the classifiers,the accuracy of recognition is 87.5%. To increase the accuracy of recognition, we should add more features for classification.


Rating: I will give myself 9.0 points. I was not able to achieve the 100%recognition accuracy due to the limited number of features that I can extract based from the image processing techniques that I know.