Fast and accurate superpixel segmentation algorithm with a guidance image

Abstract

Superpixels are generated by automatically grouping image pixels into lots and lots of compact segments. In computer vision, it is widely used as an effective way to reduce the number of image primitives for subsequent tasks and for recognizing objects’ contours due to its excellent boundary adhesion. The primary concerns of a superpixel generation algorithm are its efficiency and accuracy. In this document, we aim to propose a fast and precise superpixel segmentation algorithm with a guidance image. Specifically, we introduce a rolling filter that can remove details while well preserving the boundaries to obtain the guidance image. In the meantime, we introduce a robust edge confidence operator to accurately detect image boundaries. From this, we define a distance measurement with adaptive parameters for each image. In this way, we can adapt and accurately group pixels into regions according to the new distance measurement. Furthermore, we adopt a noniterative framework to generate superpixels in real time by processing all pixels once. The experimental results show that the proposed methodology achieves state-of-the-art performance on two sets of reference data while operating in real time.