Can neural network fail to learn a function? and How to choose better feature descriptors for pattern recognition?

Question

I was working on webots which is an environment used to model, program and simulate mobile robots. Basically i have a small robot with a VGA camera, and it looks for simple blue coloured patterns on white walls of a small lego maze and moves accordingly

The method I used here was ​

• Obtain images of the patterns from webots and save it in a location in PC.
• ​​Detect the blue pattern, form a square enclosing the pattern with atleast 2 edges of the pattern being part of the boundary of the square.

• ​Resize it to 7x7 matrix(using nearest neighbour interpolation algorithm)

• The input to the network is nothing but the red pixel intensities of each of the 7x7 image(when i look at the blue pixel through a red filter it appears black so). The intensities of each pixel is extracted and the 7x7 matrix is then converted it to a 1D vector ie 1x49 which is my input to the neural network. (I chose this characteristic as my input because it is 'relatively' less difficult to access this information using C and webots.​​)

I used MATLAB for this offline training method and I used a slower learning rate(0.06) to ensure parameter convergence and tested it on large and small datasets(1189 and 346 respectively). On all the numerous times I have tried, the network fails to classify the pattern.(it says the pattern belongs to all the 4 classes !!!! ) . There is nothing wrong with the program as I tested it out on the simpleclass_dataset in matlab and it works almost perfectly

Is it possible that the neural network fails to learn the function because of really poor data? (by poor data i mean that the datapoints corresponding to one sample of one class are very close to another sample belonging to a different class or something of that sort). Or can the neural network fail because of very poor feature descriptors?

Can anyone suggest a simpler method to extract features from the image(I am now shifting to MATLAB as I am now only concerned with simulations in webots and not the real robot). What sort of features can I choose? The patterns are very simple (L,an inverted L and its reflected versions are the 4 patterns)

Answer 1

Neural networks CAN fail to learn a function; this is most often caused by employing a network topology which is too simple to model the necessary function. A classic example of this case is attempting to learn an XOR function using a perceptron classifier , although it can even happen in multilayer neural nets sometimes; especially for complex tasks like image recognition. See my previous answer for a rough guide on how to select neural network parameters (ignore the convolution stuff if you want, although I would highly recommened looking into convolutional neural networks if you are still having problems).

It is a possiblity that there is too little seperability between classes, although I doubt that this is the case given your current features. Is there a reason that your network needs to allow an image to be four classifications simultaneously? If not, then perhaps you could classify the input as the output with the highest activation instead of all those with high activations.