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POClassifiying a set of Images into Classes
 

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  1. POClassifiying a set of Images into Classes
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    <p>I have the problem that I get a set of pictures and need to classify those. </p> <p>The thing is, i do not really have any knowledge of these images. So i plan on using as many descriptors as I can find and then do a PCA on those to identify only the descriptors that are of use to me. </p> <p>I can do supervised learning on a lot of datapoints, if that helps. However there is a chance that pictures are connected to each other. Meaning there could be a development from Image X to Image X+1, although I kinda hope this gets sorted out with the information in each Image. </p> <p>My question are: </p> <ol> <li>How do i do this best when using Python? (I want to make a proof of concept first where speed is a non-issue). What libraries should i use? </li> <li>Are there examples already for an image Classification of such a kind? Example of using a bunch of descriptors and cooking them down via PCA? This part is kinda scary for me, to be honest. Although I think python should already do something like this for me.</li> </ol> <p>Edit: I have found a neat kit that i am currently trying out for this: <a href="http://scikit-image.org/" rel="noreferrer">http://scikit-image.org/</a> There seem to be some descriptors in there. Is there a way to do automatic feature extraction and rank the features according to their descriptive power towards the target classification? PCA should be able to rank automatically.</p> <p>Edit 2: I have my framework for the storage of the data now a bit more refined. I will be using the Fat system as a database. I will have one folder for each instance of a combination of classes. So if an image belongs to class 1 and 2, there will be a folder img12 that contains those images. This way i can better control the amount of data i have for each class.</p> <p>Edit 3: I found an example of a libary (sklearn) for python that does some sort of what i want to do. it is about recognizing hand-written digits. I am trying to convert my dataset into something that i can use with this. </p> <p>here is the example i found using sklearn:</p> <pre><code>import pylab as pl # Import datasets, classifiers and performance metrics from sklearn import datasets, svm, metrics # The digits dataset digits = datasets.load_digits() # The data that we are interested in is made of 8x8 images of digits, # let's have a look at the first 3 images, stored in the `images` # attribute of the dataset. If we were working from image files, we # could load them using pylab.imread. For these images know which # digit they represent: it is given in the 'target' of the dataset. for index, (image, label) in enumerate(zip(digits.images, digits.target)[:4]): pl.subplot(2, 4, index + 1) pl.axis('off') pl.imshow(image, cmap=pl.cm.gray_r, interpolation='nearest') pl.title('Training: %i' % label) # To apply an classifier on this data, we need to flatten the image, to # turn the data in a (samples, feature) matrix: n_samples = len(digits.images) data = digits.images.reshape((n_samples, -1)) # Create a classifier: a support vector classifier classifier = svm.SVC(gamma=0.001) # We learn the digits on the first half of the digits classifier.fit(data[:n_samples / 2], digits.target[:n_samples / 2]) # Now predict the value of the digit on the second half: expected = digits.target[n_samples / 2:] predicted = classifier.predict(data[n_samples / 2:]) print("Classification report for classifier %s:\n%s\n" % (classifier, metrics.classification_report(expected, predicted))) print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted)) for index, (image, prediction) in enumerate( zip(digits.images[n_samples / 2:], predicted)[:4]): pl.subplot(2, 4, index + 5) pl.axis('off') pl.imshow(image, cmap=pl.cm.gray_r, interpolation='nearest') pl.title('Prediction: %i' % prediction) pl.show() </code></pre>
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    1. COAnd what did you try so far? Show some effort mate.
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    2. COi will edit the stuff in that i accomplish so far.
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