As a starting point, you can think about unsuvervized image classification as a type of image clustering. You can - for instance - use VGG16 weights, extract image pseudo-features, and run some clustering on this feature set.
Here is some "starter code" (Python):
# Imports
from keras.preprocessing import image
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
import numpy as np
from sklearn.cluster import KMeans
import os, shutil, glob, os.path
from PIL import Image as pil_image
image.LOAD_TRUNCATED_IMAGES = True
model = VGG16(weights='imagenet', include_top=False)
# Variables
imdir = 'C:/indir/' # DIR containing images
targetdir = "C:/outdir/" # DIR to copy clustered images to
number_clusters = 10
# Loop over files and get features
filelist = glob.glob(os.path.join(imdir, '*.jpg'))
filelist.sort()
featurelist = []
for i, imagepath in enumerate(filelist):
try:
print(" Status: %s / %s" %(i, len(filelist)), end="\r")
img = image.load_img(imagepath, target_size=(224, 224))
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
features = np.array(model.predict(img_data))
featurelist.append(features.flatten())
except:
continue
# Clustering
kmeans = KMeans(n_clusters=number_clusters, random_state=0).fit(np.array(featurelist))
# Copy images renamed by cluster
# Check if target dir exists
try:
os.makedirs(targetdir)
except OSError:
pass
# Copy with cluster name
print("\n")
for i, m in enumerate(kmeans.labels_):
try:
print(" Copy: %s / %s" %(i, len(kmeans.labels_)), end="\r")
shutil.copy(filelist[i], targetdir + str(m) + "_" + str(i) + ".jpg")
except:
continue