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copied!<p>You can convert a picture to a vector of pixels, and perform PCA on that vector. This might be easier than trying to manually find descriptors. You can use numPy and sciPy in python. For example:</p> <pre><code>import scipy.io from numpy import * #every row in the *.mat file is 256*256 numbers representing gray scale values #for each pixel in an image. i.e. if XTrain.mat has 1000 lines than each line #will be made up of 256*256 numbers and there would be 1000 images in the file. #The following loads the image into a sciPy matrix where each row is a vector #of length 256*256, representing an image. This code will need to be switched #out if you have a different method of storing images. Xtrain = scipy.io.loadmat('Xtrain.mat')["Xtrain"] Ytrain = scipy.io.loadmat('Ytrain.mat')["Ytrain"] Xtest = scipy.io.loadmat('Xtest.mat')["Xtest"] Ytest = scipy.io.loadmat('Ytest.mat')["Ytest"] learn(Xtest,Xtrain,Ytest,Ytrain,5) #this lowers the dimension from 256*256 to 5 def learn(testX,trainX,testY,trainY,n): pcmat = PCA(trainX,n) lowdimtrain=mat(trainX)*pcmat #lower the dimension of trainX lowdimtest=mat(testX)*pcmat #lower the dimension of testX #run some learning algorithm here using the low dimension matrices for example trainset = [] knnres = KNN(lowdimtrain, trainY, lowdimtest ,k) numloss=0 for i in range(len(knnres)): if knnres[i]!=testY[i]: numloss+=1 return numloss def PCA(Xparam, n): X = mat(Xparam) Xtranspose = X.transpose() A=Xtranspose*X return eigs(A,n) def eigs(M,k): [vals,vecs]=LA.eig(M) return LM2ML(vecs[:k]) def LM2ML(lm): U=[[]] temp = [] for i in lm: for j in range(size(i)): temp.append(i[0,j]) U.append(temp) temp = [] U=U[1:] return U </code></pre> <p>In order to classify your image you can used k-nearest neighbors. i.e. you find the k nearest images and label your image with by majority vote over the k nearest images. For example:</p> <pre><code>def KNN(trainset, Ytrainvec, testset, k): eucdist = scidist.cdist(testset,trainset,'sqeuclidean') res=[] for dists in eucdist: distup = zip(dists, Ytrainvec) minVals = [] sumLabel=0; for it in range(k): minIndex = index_min(dists) (minVal,minLabel) = distup[minIndex] del distup[minIndex] dists=numpy.delete(dists,minIndex,0) if minLabel == 1: sumLabel+=1 else: sumLabel-=1 if(sumLabel>0): res.append(1) else: res.append(0) return res </code></pre>

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