I am a beginner in the field of image processing and machine learning und I hope I’m here in the right forum.
It’s about training an SVM (support vector machine) with hog features from images which are reduced by PCA (principal component analysis). The Hog features are extracted from the raw pixel as follows:
from skimage import feature ... feat = feature.hog(image, orientations=12, pixels_per_cell=(4,4), cells_per_block=(2,2), block_norm='L2_Hys', transform_sqrt=True) ...
The hog feature vectors contain only pixel values between 0 and 1, so it seems that they are normalized. Therefore I’m not sure if I should really apply the often recommended StandardScaler before applying PCA?