The human brain manages information by forming paths for that certain information and thus 'learns' the information unlike LDA and the Minimum Distance which relies heavily on the features of the object. Examples and iterations develops or trains the network thus speeds up recognition. The framework of a Neural Network is shown below.
Figure 1. Neural Network Framework
A Neural Network is composed of three layers which are the input layer, hidden layer, and the output layer. The number of inputs in the input layer is determined by the number of features while the hidden layer can be composed of any desired number of nodes. The input receives the data/signals/features which in turn is forwarded to the hidden layer which acts on the inputs and finally passes the results to the output layer.
In this activity we feed the obtained features of our objects to a neural network composed of 3 input nodes that represent the 3 features used namely, area, perimeter and RGB sum. There are also 3 hidden nodes, and an output node. The learning rate used is 0.5 and the number of iterations is 1000.
To train the Neural Network we first entered into the network the training set and then followed by the test set. Figure 2 below shows the results obtained from the Neural Network. To implement this method, the code used by the previous AP186 class, made by Mr. Jeric Tugaff, was utilized. The classification showed 100% accuracy. Changing the order of the objects is also an interesting direction that can be took which might challenge the accuracy of the method since the Neural Network is not trained enough for recognition. I give myself a 10 for this activity.
Figure 2. Resulting classification obtained using Neural Networks.
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