Fast Graph Algorithms for Superpixel Segmentation

We introduce the novel graph-based algorithm SLAM (simultaneous local assortative mixing) for fast and high-quality superpixel segmentation of any large color image. Super-pixels are compact semantic image elements; superpixel segmen-tation is fundamental to a broad range of vision tasks in existing and emerging applications, especially, to safety-critical and time-critical applications. SLAM leverages a graph representation of the image, which encodes the pixel features and similarities, for its rich potential in implicit feature transformation and extra means for feature differentiation and association at multiple resolution scales. We demonstrate, with our experimental results on 500 benchmark images, that SLAM outperforms the state-of-art algorithms in superpixel quality, by multiple measures, within the same time frame. The contributions are at least two-fold: SLAM breaks down the long-standing speed barriers in graph-based algorithms for superpixel segmentation; it lifts the fundamental limitations in the feature-point-based algorithms.