semantic segmentation using kmeans or mean shift

i know what semantic segmentation is and i know how to do semantic segmentation using deep learning but my question here can i do semantic segmentation with a traditional way like kmeans or mean shift ckustering? here's what i tried to do

import numpy as np
import cv2
from sklearn.cluster import MeanShift, estimate_bandwidth
#from skimage.color import rgb2lab
#Loading original image
originImg = cv2.imread('test/2019_00254.jpg')

# Shape of original image
originShape = originImg.shape


# Converting image into array of dimension [nb of pixels in originImage, 3]
# based on r g b intensities
flatImg=np.reshape(originImg, [-1, 3])


# Estimate bandwidth for meanshift algorithm
bandwidth = estimate_bandwidth(flatImg, quantile=0.1, n_samples=100)
ms = MeanShift(bandwidth = bandwidth, bin_seeding=True)

# Performing meanshift on flatImg
ms.fit(flatImg)

# (r,g,b) vectors corresponding to the different clusters after meanshift
labels=ms.labels_

# Remaining colors after meanshift
cluster_centers = ms.cluster_centers_

# Finding and diplaying the number of clusters
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
print(number of estimated clusters : %d % n_clusters_)

segmentedImg = cluster_centers[np.reshape(labels, originShape[:2])]
cv2.imshow('Image',segmentedImg.astype(np.uint8))
cv2.waitKey(0)
cv2.destroyAllWindows()

but this is only making segmentation for the whole image