Classification of Painting styles

To try our algorithm on another possible application, we took 35 pictures (about 600x800 pixels size) drawn by 5 different painters, trying to identify the painters by their drawing style. In the experiments we have carried out, each of the painters was classified correctly, and the obtained clusters were stable. We used windows of 128x128 and 64x64 pixels, overlapping and not overlapping, with different wavelet filters, and always converged to the same result: Figure 11.

The obtained clusters differentiate between impressionism style of Van Gogh, characterized by a large amount of bold brush-strokes, classic style of Rembrandt, with its soft lines and smooth textures, landscape reproductions of Shishkin, rich of small scrupulous details, cubism of Picasso and marine landscapes of Aivazovsky.

It should be noted that in this example the correct classification was obtained only when we took sufficiently fine segmentation of the input images (into about 20-25 segments). In order to choose a segmentation with an appropriate number of segments for the subsequent classification step, we looked at the inverse rate distortion function D(R), as described in the paper (Figure 12).
 
The points where the decrease of the distortion slows down relative to the increase in the mutual information correspond to stable segmentation solutions. The vertical grid lines in the graph correspond to the increments of the number of segments in the partition by one. By looking at the minima of the second derivative of the function, we identify stabilization points in the segmentation solution. In the example shown, one of the evident (actually most evident) local minima corresponds to segmentation into 24 segments, which is actually the point where the classification results become correct and stabilize. When taking segmentations with a greater number of segments we always converged to the same correct classification of the input images. At the same time, when classifying images using segmentation into less than 24 segments, a small number of misclassifications were present.

When using large (256x256 and more) windows, at all the segmentation resolutions in subsequent classification some of the pictures were misclassified (for example, some of the Picasso paintings were mixed up with those of Aivazovsky). As well, we got poor results when used one big window of the whole image size, which is equivalent to classifying the images without segmenting them first. These results justify the advantage of image segmentation as a preprocessing step for classification.