GLCM Texture Features¶
This example illustrates texture classification using grey level co-occurrence matrices (GLCMs) 1. A GLCM is a histogram of co-occurring greyscale values at a given offset over an image.
In this example, samples of two different textures are extracted from an image: grassy areas and sky areas. For each patch, a GLCM with a horizontal offset of 5 (distance= and angles=) is computed. Next, two features of the GLCM matrices are computed: dissimilarity and correlation. These are plotted to illustrate that the classes form clusters in feature space. In a typical classification problem, the final step (not included in this example) would be to train a classifier, such as logistic regression, to label image patches from new images.
Haralick, RM.; Shanmugam, K., “Textural features for image classification” IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621. DOI:10.1109/TSMC.1973.4309314
import matplotlib.pyplot as plt from skimage.feature import greycomatrix, greycoprops from skimage import data PATCH_SIZE = 21 # open the camera image image = data.camera() # select some patches from grassy areas of the image grass_locations = [(280, 454), (342, 223), (444, 192), (455, 455)] grass_patches =  for loc in grass_locations: grass_patches.append(image[loc:loc + PATCH_SIZE, loc:loc + PATCH_SIZE]) # select some patches from sky areas of the image sky_locations = [(38, 34), (139, 28), (37, 437), (145, 379)] sky_patches =  for loc in sky_locations: sky_patches.append(image[loc:loc + PATCH_SIZE, loc:loc + PATCH_SIZE]) # compute some GLCM properties each patch xs =  ys =  for patch in (grass_patches + sky_patches): glcm = greycomatrix(patch, distances=, angles=, levels=256, symmetric=True, normed=True) xs.append(greycoprops(glcm, 'dissimilarity')[0, 0]) ys.append(greycoprops(glcm, 'correlation')[0, 0]) # create the figure fig = plt.figure(figsize=(8, 8)) # display original image with locations of patches ax = fig.add_subplot(3, 2, 1) ax.imshow(image, cmap=plt.cm.gray, vmin=0, vmax=255) for (y, x) in grass_locations: ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs') for (y, x) in sky_locations: ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs') ax.set_xlabel('Original Image') ax.set_xticks() ax.set_yticks() ax.axis('image') # for each patch, plot (dissimilarity, correlation) ax = fig.add_subplot(3, 2, 2) ax.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go', label='Grass') ax.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo', label='Sky') ax.set_xlabel('GLCM Dissimilarity') ax.set_ylabel('GLCM Correlation') ax.legend() # display the image patches for i, patch in enumerate(grass_patches): ax = fig.add_subplot(3, len(grass_patches), len(grass_patches)*1 + i + 1) ax.imshow(patch, cmap=plt.cm.gray, vmin=0, vmax=255) ax.set_xlabel('Grass %d' % (i + 1)) for i, patch in enumerate(sky_patches): ax = fig.add_subplot(3, len(sky_patches), len(sky_patches)*2 + i + 1) ax.imshow(patch, cmap=plt.cm.gray, vmin=0, vmax=255) ax.set_xlabel('Sky %d' % (i + 1)) # display the patches and plot fig.suptitle('Grey level co-occurrence matrix features', fontsize=14, y=1.05) plt.tight_layout() plt.show()
Total running time of the script: ( 0 minutes 1.035 seconds)