  Superpixel algorithms grouping pixels with similar color and other low-level
properties are increasingly used for pre-processing in image segmentation. In
recent years, a focus has been placed on developing geometric superpixel
methods that facilitate the extraction and analysis of geometric image
features. Diagram-based superpixel methods are important among the geometric
methods as they generate compact and sparsely representable superpixels.
Introducing generalized balanced power diagrams to the field of superpixels, we
propose a diagram method called Power-SLIC. Power-SLIC is the first geometric
superpixel method to generate piecewise quadratic boundaries. Its speed,
competitive with fast state-of-the-art methods, is unprecedented for diagram
approaches. Extensive computational experiments show that Power-SLIC
outperforms existing diagram approaches in boundary recall, under segmentation
error, achievable segmentation accuracy, and compression quality. Moreover,
Power-SLIC is robust to Gaussian noise.
