Comparison of node affinity to sub-graph affinity.

<p>This figure illustrates a pixel-to-pixel rectangular sub-graph specific to implementation for an image and the corresponding edge weight equations. (a) For node affinity, each node, , (red) is compared to a neighbouring node, (green), for all nodes in the local neighborhood, . (b) If neighborhood statistics, such as a mean or weighted average, were applied as preprocessing to the data or image, the resulting similarity metric would contain regional statistics and would be more robust to noise with the cost of losing fine details such as edges and textures. (c) Rather than calculating the similarity of two regions from a single statistic, as in (b), sub-graph affinity calculates the similarity of two regions by calculating the similarity of each corresponding element within the regions, improving robustness to noise while preserving fine details of the region. Using sub-graph affinity, two regions with the same average intensity, but with differing textures, can be classified as two different classes.