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Semantic image segmentation is a problem of simultaneous segmentation and recognition of an input image into regions and their associated categorical labels, such as person, car or cow. A popular way to achieve this goal is to assign a label to every pixel in the input image and impose simple structural constraints on the output label space. Efficient approximation algorithms for solving this labelling problem such as α-expansion have, at best, linear runtime complexity with respect to the number of labels, making them practical only when working in a specific domain that has few classes-of-interest. However when working in a more general setting where the number of classes could easily reach tens of thousands, sub-linear complexity is desired. In this paper we propose meeting this requirement by performing cascaded inference that wraps around the α-expansion algorithm. The cascade both divides the large label set into smaller more manageable ones by way of a hierarchy, and dynamically subdivides the image into smaller and smaller regions during inference. We test our method on the SUN09 dataset with 107 accurately hand labelled classes.

Scalable Cascade Inference for Semantic Image Segmentation