Content-Aware Dynamic Superpixel Segmentation

Abstract

In recent years, deep learning-based superpixel segmentation methods derived from SLIC have made significant progress by utilizing uniform grid-based seed initialization. However, due to the unequal pixel space variation rates in natural images, methods based on uniform grid initialization struggle to balance the compactness of superpixels in flat regions with the boundary adherence in non-flat regions. Inspired by the visual attention model based on saliency in the human visual system, we propose a content-aware dynamic superpixel segmentation network. Specifically, we propose a seed initialization strategy guided by geodesic distance transformation and design two segmentation heads for different scales, which are used for joint network training to encourage the network to focus more on areas with texture variations without causing unnecessary segmentation in flat regions. Extensive experiments on BSDS500 and NYUv2 datasets demonstrate that our method achieves state-of-the-art performance.