Maximin affinity learning of image segmentation

TL;DR

This paper introduces a novel machine learning approach that trains affinity classifiers to directly optimize segmentation quality by minimizing the Rand index, improving image segmentation accuracy.

Contribution

It presents the first algorithm for training affinity classifiers to directly minimize segmentation errors measured by the Rand index.

Findings

The method effectively improves segmentation quality.

It directly optimizes a segmentation performance measure.

The approach outperforms previous affinity learning methods.

Abstract

Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.