Grid Seams: A Fast Superpixel Algorithm for Real-Time Applications

Super pixels are a compact and simple representation of images that has been used for many computer vision applications such as object localization, segmentation and depth estimation. While useful as compact representations of images, the time complexity of super pixel algorithms has prevented their use in real-time applications like video processing. Fast super pixel algorithms have been proposed recently but they lack regular structure or required accuracy for representing image structure. We present Grid Seams, a novel seam carving approach to super pixel generation that preserves image structure information while enforcing a global spatial constraint in the form of a grid structure cost. Using a standard dataset, we show that our approach is faster than existing approaches and can achieve accuracies close to a state-of-the-art super pixel generation algorithms.