# Develop a simple image classification system by using Laplacian of Gaussian (LoG)

I am tring to write a simple CAD system to classify some images within two groups by using Laplacian of Gaussian (LoG). I am using scikit-image for this tasks and I want to use DNN in keras to train a classifer.

The problem is that I am not sure how to create feature list by using LOG.

My images could have different sizes so LOG could have different feature set.

I am using below codes

from math import sqrt
from skimage import data
from skimage.feature import blob_dog, blob_log, blob_doh
from skimage.color import rgb2gray

img1T = Image.open( "C:\Users\Public\h1T.jpg")
img2T=Image.open( "C:\Users\Public\h2T.jpg")
data_img1T = np.asarray( img1T , dtype='uint8' )
data_img2T= np.asarray( img2T, dtype='uint8' )
data_gray_img1T = rgb2gray(data_img1T)
data_gray_img2T = rgb2gray(data_img2T)

blobs_log_img1T = blob_log(data_gray_img1T, max_sigma=30, num_sigma=10, threshold=.1)
blobs_log_img2T = blob_log(data_gray_img2T, max_sigma=30, num_sigma=10, threshold=.1)


when getting the LOG feature for tow images:

>>> blobs_log_img1T.shape
(73L, 3L)
>>> blobs_log_img2T.shape
(51L, 3L)


now these two images have different shapes, but I need to find a way to use identical number of features for all images. Do I have to resize images to be of identical sizes or I have to take another approach?

Thanks,