将一组图像分类为类

Question

我有一个问题,我得到一组图片,需要对这些图片进行分类.

问题是,我真的不知道这些图像.因此,我计划使用尽可能多的描述符,然后对其进行PCA以仅识别对我有用的描述符.

如果有帮助,我可以对很多数据点进行监督学习.但是,图片有可能相互连接.这意味着可能有从Image X到Image X + 1的开发,尽管我希望这可以通过每个Image中的信息进行整理.

我的问题是:

1. 使用Python时如何做到最好？(我想首先在速度不成问题的情况下进行概念验证).我应该使用哪些库？
2. 是否有图像分类这样的例子？使用一堆描述符并通过PCA将其烹饪下来的示例？说实话,这部分对我来说有点吓人.虽然我认为python应该已经为我做了类似的事情.

编辑:我找到了一个整洁的工具包,我目前正在尝试这个:http://scikit-image.org/那里似乎有一些描述符.有没有办法进行自动特征提取,并根据对目标分类的描述能力对特征进行排名？PCA应该能够自动排名.

编辑2:我的数据存储框架现在更加精细了.我将使用Fat系统作为数据库.我将为每个类组合的实例提供一个文件夹.因此,如果图像属于第1类和第2类,则会有一个包含这些图像的文件夹img12.这样我就可以更好地控制每个班级的数据量.

编辑3:我找到了一个python的libary(sklearn)的例子,它做了我想做的事情.它是关于识别手写数字.我正在尝试将我的数据集转换为我可以使用的数据集.

这是我发现使用sklearn的例子:

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()

Answer 1

您可以将图片转换为像素矢量,并在该矢量上执行PCA.这可能比尝试手动查找描述符更容易.你可以在python中使用numPy和sciPy.例如:

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

为了对图像进行分类,您可以使用k近邻.即你找到k个最近的图像,并通过对k个最近的图像的多数投票来标记你的图像.例如:

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