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:
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
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:
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