Superpixel Segmentation With Edge Guided Local-Global Attention Network

Superpixel segmentation aims to automatically group visually similar pixels within an image into compact regions. This approach provides an efficient low-level representation of image data, effectively reducing the complexity of image primitives for subsequent vision tasks. Recent deep convolutional networks have shown their advantages in superpixel segmentation task. However, many existing deep learning methods still struggle to preserve object edges and accurately perceive similar pixels. This limitation can be attributed to their inadequate ability to model edge information and capture effective context within the image. To address these issues, we propose an Edge guided Local-Global Attention Network (ELGANet) for superpixel segmentation. Specifically, we first devise an Edge Enhancement Module (EeEM), which integrates multiple edge features into the superpixel-friendly features. Then, we develop a Local-Global Attention Module (LGAM) to analyze the relationship between pixels and local or global region patches, expecting to obtain effective context information for grouping similar pixels. The edge features and deep global semantic features are subsequently fused to generate the superpixel-friendly features. The final superpixel-friendly features are then mapped into final superpixels. Extensive experiments on four benchmark datasets demonstrate the effectiveness and superiority of our ELGANet compared with ten state-of-the-art models.