Classify binary images with multiple features

I'm trying to classify a set of binary images in matlab. I have created a model using a lot of different images and have classified each one accordingly so that the model 'knows' which images belong to which class. Each image is represent as a feature vector, which is 1x59. I am successfully able to calculate the mean and covariance of class of images. The classifier uses maximum a posterior classification (MAP), but for some reason all the images in the test aren't being classified properly. The long equation I am using in the variable 'score' is the MAP calculation . Does anyone have any suggestions as to how to best classify an image?

function [classname] = classify(imagepath)
%Classify the image specified for the given image path

    load('models');

    %Assume all models use the same number of features
    N = length(models(1).mean);

    %(N+1)/2 because features considers positive and negative fourier
    %therefore produces double the number of feature vectors
    features = getFeatures(imagepath, ((N+1)/2)); 

    maxscore = -inf;

    %Find out which class has the highest score
    for index = 1:length(models)
        model = models(index);

        %this should give a probablist score
        score = max(log(model.prior) - 0.5*log(abs(model.cov)) - 0.5*(features - model.mean)'*inv(model.cov)*(features - model.mean));

        %Score should be a 1x1 array not that monster
        if score > maxscore
            maxscore = score;
            bestindex = index;
        end
    end

    classname = classes(bestindex);
end

Answer 1

[Prajit T R]

Hi Edward

The Deep Learning toolbox can help in classifying images with a good accuracy. Check out this link: Image classification using Deep learning There's another documentation link wherein an example with the use of SVM for image classification is given. You may want to try that as well: SVM for Image classification

Hope this helps.

Cheers