Adaptive high-precision superpixel segmentation

Superpixel as a fundamental processing unit can significantly reduce the computational complexity of subsequent computer vision tasks. In this paper, an Adaptive High-Precision (AHP) superpixel segmentation algorithm is proposed. Three major schemes are proposed in this algorithm. First, a scheme of generating the initial number of superpixels adaptively is proposed by calculating the histogram peak value of L-component corresponding to the smallest salient region. In addition, we show that the isolated pixels are not only existent but also abundant. Instead of using distance-measurement of color and position space, we reclassify each isolated pixel just relying on the LAB color space. Furthermore, the detection and re-segmentation scheme of the under-segmentation superpixels is adopted according to the standard deviation and mean value of the L-component, which can re-segment each under-segmentation superpixel directly without measuring the distance between each under-segmentation pixel and new centroid. The proposed algorithm has linear computational complexity and the merits of adaptivity and high precision. Experiments on the Berkeley segmentation dataset demonstrate the effectiveness and feasibility of the proposed schemes, and they also prove AHP can achieve high-precision superpixel segmentation results in comparison with the state-of-the-art algorithms on standard metrics.