image analysis theory: mapping a network

This is a discussion on image analysis theory: mapping a network within the C++ Programming forums, part of the General Programming Boards category; Lets say you have a set of images and a set of image features, the image features are a huge ...

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    image analysis theory: mapping a network

    Lets say you have a set of images and a set of image features, the image features are a huge set of various statistics performed on the image. You are trying to use image features to classify these images, and these image features may be inter-related. We are essentially trying to produce a graph that shows that these features are dependant (positively or negatively) with one another.

    Lets say there are 5 image types, and 1000 features.
    Lets say you had enough data to calculate how each feature was related to the type of image. Say, the image analysis algorithm is represented by a formula R(f) for f=(1 ... 1000), but features can be corelated. What strategies are there to produce the neural network graph for problem? See the following diagram:

    Code:
    http://upload.wikimedia.org/wikipedia/commons/e/e4/Artificial_neural_network.svg
    Is there a particular mathematical field that deals with this? What is it called? Combinatronics?
    Hope this makes some sense...

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    Captain Crash brewbuck's Avatar
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    Is there a particular mathematical field that deals with this? What is it called? Combinatronics?
    Data mining.

    The type of representation you are looking for is a Bayesian belief network, although there are many different ways a large set of features can be analyzed in terms of their predictive power to a class variable. Decision trees can prove useful but they are generally unable to model simultaneous interactions between features.

    For starters I would download the WEKA package and figure out how to massage your data into ARFF format so WEKA can read it. Then you can experiment with hundreds of different data mining algorithms. I'd start with some basic information gain analysis (attribute selection), maybe look at PCA, to figure out where the majority of the information lies. Then you can trim your feature set and start feeding instances into classification learners.
    Code:
    //try
    //{
    	if (a) do { f( b); } while(1);
    	else   do { f(!b); } while(1);
    //}

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    Is that your rug-rat, brewbuck? Cute kid. I know that is off topic, but I have a short attention span.

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    Captain Crash brewbuck's Avatar
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    Quote Originally Posted by master5001 View Post
    Is that your rug-rat, brewbuck? Cute kid. I know that is off topic, but I have a short attention span.
    It is. He's 14 months now! He wasn't walking yet in that photo.
    Code:
    //try
    //{
    	if (a) do { f( b); } while(1);
    	else   do { f(!b); } while(1);
    //}

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    Banned master5001's Avatar
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    I bet life was a little less stressful back when he wasn't walking yet. I can't tell if he has a book or a keyboard. I am leaning towards keyboard.

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    weka is in java. What do you think of R statistical programming language (its based on S)?

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