Rethinking superpixel segmentation from biologically inspired mechanisms

Abstract

Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation. However, a significant challenge remains in generating superpixels that strictly adhere to object boundaries while conveying rich visual significance, especially with limited training data, leading to the generation of blunt superpixel that encompass different semantics. To address these challenges, we propose a novel bio-inspired superpixel segmentation network (BINet), drawing inspiration from neural structures and visual mechanisms. Specifically, the BINet includes an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating visual cortex interactive projection mechanisms, while the BAL emulates spatial frequency characteristics of visual cortical cells to generate superpixels with strong boundary adherence. Extensive experiments on BSDS500, NYUv2 and KITTI datasets show that our method achieves state-of-the-art performances but maintain satisfactory inference efficiency. Our code is available at https://github.com/zhaotingyu-ss/BINet.