Classifying images using backpropagation algorithm

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Hello,
Im working on an ANN project in which iam asked to classify and train a network that has 3 images as an input.
These images are: apple, banana, and a mobile.
The images has to be inputed pixel by pixel, and each image be trained in a way that the system must know that a red apple is an apple, or the green apple is an apple, etccc.

Second, I dont know how many hidden layers and how many neurons in each hidden layer I have to take (I started with one hidden layer and 6 hidden neurons.

It is a must that we use the Back propagation algorithm for the classification of the three images.

I Am writing the code in C#, but I have some problems in coding the project especially how to tell the system that it must know that the green and the yellow apples are apples and will not be able to recognize the green apple since we trained the system to recognize the red apple. ( I want it to recognize all colored apples as apples).

Also, assuming that Iam taking a 256 * 256 image, and each pixel is an input, then we have around 65000 input, which means we have 65000 weights for each neuron in the hidden layer, and I started with 6 neurons. Does it make sense?

Could any one help in the coding please?

Posted 21-May-11 2:04am

Mohammad Shehab

Updated 21-May-11 2:16am

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Christian Graus · Accepted Answer · 2011-05-21T02:14:00

Solution 1

It seems to me that an apple is round, a banana bent, and a mobile is square. Beyond that, if it's your school project, I don't think it's appropriate for people to give you code, unless it's in specific reply to specific code that you post here.

Posted 21-May-11 2:14am

Christian Graus

Comments

Mohammad Shehab 21-May-11 8:18am

so if an apple is round, a banana bent, and a mobile is square, what should i do?

Mohammad Shehab 22-May-11 15:50pm

Ok, I think that this idee gave the solution to my problem. Thanks!