作为我在机器学习中的学习项目，我正在尝试使用SVM（支持向量机）对多米诺瓷砖的不同图像进行分类。我将这个项目很大程度上建立在我重新创建并理解的项目https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py的基础上，并获得了大约70％的precision / F1（如果我没记错的话）。我在项目中使用了很多相同的代码。
在我的项目中，我有28个不同的文件夹，并且在每100个不同的100x100px的多米诺瓷砖图像中（即2800张图像）。用不同的背景，不同的缩放比例和不同的旋转度拍摄多米诺瓷砖。这些图像可以在这里找到：https://www.kaggle.com/wallcloud/photographs-of-28-different-domino-tiles
我已经测试过：
尽管如此，我仍然无法使自己的分数超过10％，这与Scikit-Learn项目在脸上所取得的成绩相差甚远。
根据我从经验丰富的机器学习工程师那里获得的反馈，这些数据应该足以对多米诺骨牌进行分类。我怀疑SVM：s是否真的适合作为图像分类器，但是当Scikit-Learn项目使用它时，我也认为这也应该工作。我确信CNN可以很好地解决这个问题，但这不是我的问题。
当我输出多米诺骨牌的“特征面”时，它们显示为“运动模糊”，这似乎与旋转的多米诺骨牌有关。这可能是一个潜在原因（Scikit-Learn的面部图像未旋转）。但是，我希望模型能更好地适应多米诺骨牌的点，但这种假设可能是错误的。
我的问题是：
问：考虑到数据的数量和类型以及使用SVM作为分类器，我的预期得分是10％吗？还是我错过了一些关键的东西？import time
import matplotlib.pyplot as plt
from sklearn import svm, metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
#from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
from sklearn.decomposition import PCA
import numpy as np
import os # Working with files and folders
from PIL import Image # Image processing
from PIL import ImageFilter
###
### Data can be downloaded from https://www.dropbox.com/sh/s5f38k4l2on5mba/AACNQgXuw1edwEb6oO1w3CfOa?dl=0
###
start = time.time()
rootdir = os.getcwd()
image_file = 'images.npy'
key_file = 'keys.npy'
def predict_me(image_file_name, scaler, pca):
pm = Image.open(image_file_name)
pm = pm.resize([66,66])
a = np.array(pm.convert('L')).reshape(1,-1)
#a = np.array(pm.resize([66,66]).convert('L')).reshape(1,-1)) # array 66x66
a = scaler.transform(a)
a = pca.transform(a)
return classifier.predict(a)
def crop_image(im, sq_size):
new_width = sq_size
new_height = sq_size
width, height = im.size # Get dimensions
left = (width - new_width)/2
top = (height - new_height)/2
right = (width + new_width)/2
bottom = (height + new_height)/2
imc = im.crop((left, top, right, bottom))
return imc
#def filter_image(im):
# All filter makes it worse
#imf = im.filter(ImageFilter.EMBOSS)
#return imf
def provide_altered_images(im):
im_list = [im]
im_list.append(im.rotate(90))
im_list.append(im.rotate(180))
im_list.append(im.rotate(270))
return im_list
if (os.path.exists(image_file) and os.path.exists(key_file)):
print("Loading existing numpy's")
pixel_arr = np.load(image_file)
key = np.load(key_file)
else:
print("Creating new numpy's")
key_array = []
pixel_arr = np.empty((0,66*66), "uint8")
for subdir, dirs, files in os.walk('data'):
dir_name = subdir.split("/")[-1]
if "x" in dir_name:
for file in files:
if ".DS_Store" not in file:
im = Image.open(os.path.join(subdir, file))
if im.size == (100,100):
use_im = crop_image(im,66) # Most images are shot from too far away. This removes portions of it.
#use_im = filter_image(use_im) # Filters image, but does no good at all
im_list = provide_altered_images(use_im) # Create extra data with 3 rotated images of every image
for alt_im in im_list:
key_array.append(dir_name) # Each image here is still the same as directory name
numpied_image = np.array(alt_im.convert('L')).reshape(1,-1) # Converts to grayscale
#Image.fromarray(np.reshape(numpied_image,(-1,100)), 'L').show()
pixel_arr = np.append(pixel_arr, numpied_image, axis=0)
im.close()
key = np.array(key_array)
np.save(image_file, pixel_arr)
np.save(key_file, key)
# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001, C=10, kernel='rbf', class_weight='balanced') # gamma and C from tests
#le = preprocessing.LabelEncoder()
#le.fit(key)
#transformed_key = le.transform(key)
transformed_key = key
X_train, X_test, y_train, y_test = train_test_split(pixel_arr, transformed_key, test_size=0.1,random_state=7)
#scaler = preprocessing.StandardScaler()
pca = PCA(n_components=500, svd_solver='randomized', whiten=True)
# Fit on training set only.
#scaler.fit(X_train)
pca.fit(X_train)
# Apply transform to both the training set and the test set.
#X_train = scaler.transform(X_train)
#X_test = scaler.transform(X_test)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print ("Fit classifier")
classifier = classifier.fit(X_train_pca, y_train)
print ("Score = " + str(classifier.score(X_test_pca, y_test)))
# Now predict the value of the domino on the test data:
expected = y_test
print ("Predicting")
predicted = classifier.predict(X_test_pca)
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, labels =list(set(key))))
end = time.time()
print(end - start)
Score = 0.09830205540661305
Predicting
Classification report for classifier SVC(C=10, cache_size=200, class_weight='balanced', coef0=0.0, decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False):
precision recall f1-score support
b'0x0' 0.22 0.44 0.30 27
b'1x0' 0.24 0.23 0.24 43
b'1x1' 0.15 0.12 0.13 49
b'2x0' 0.13 0.15 0.14 34
b'2x1' 0.16 0.16 0.16 44
b'2x2' 0.02 0.03 0.03 36
b'3x0' 0.05 0.06 0.05 36
b'3x1' 0.05 0.05 0.05 42
b'3x2' 0.08 0.09 0.08 46
b'3x3' 0.15 0.16 0.15 50
b'4x0' 0.15 0.15 0.15 40
b'4x1' 0.07 0.05 0.06 42
b'4x2' 0.02 0.02 0.02 41
b'4x3' 0.09 0.08 0.09 49
b'4x4' 0.11 0.10 0.11 39
b'5x0' 0.18 0.12 0.14 42
b'5x1' 0.00 0.00 0.00 38
b'5x2' 0.02 0.02 0.02 43
b'5x3' 0.07 0.08 0.07 36
b'5x4' 0.07 0.04 0.05 51
b'5x5' 0.11 0.14 0.12 42
b'6x0' 0.03 0.03 0.03 37
b'6x1' 0.07 0.10 0.08 31
b'6x2' 0.03 0.03 0.03 33
b'6x3' 0.09 0.07 0.08 45
b'6x4' 0.02 0.03 0.03 30
b'6x5' 0.16 0.19 0.17 37
b'6x6' 0.10 0.08 0.09 36
micro avg 0.10 0.10 0.10 1119
macro avg 0.09 0.10 0.10 1119
weighted avg 0.10 0.10 0.10 1119
115.74487614631653
答案 0 :(得分：0)
我认为原因之一是，即使您应用了PCA，也不应直接将原始图像作为SVM分类器的输入。您应该计算描述图像形状，对比度和颜色的特征并将其放入分类器中，还是使用 CNN 。制作CNN可以对图像进行分类，并且结构可以自动计算图像的特征。