Rethinking Superpixel Segmentation from Biologically Inspired Mechanisms
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 when cross-surface color correlations may interfere with objects. Drawing inspiration from neural structure and visual mechanisms, we propose a biological network architecture comprising an Enhanced Screening Module (ESM) and a novel Boundary-Aware Label (BAL) for superpixel segmentation. The ESM enhances semantic information by simulating the interactive projection mechanisms of the visual cortex. Additionally, the BAL emulates the spatial frequency characteristics of visual cortical cells to facilitate the generation of superpixels with strong boundary adherence. We demonstrate the effectiveness of our approach through evaluations on both the BSDS500 dataset and the NYUv2 dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
SegmentationSuperpixelsSimilar Papers 제목 키워드 기반
Superpixel Segmentation: A Long-Lasting Ill-Posed Problem
For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such constrained segmentation problem would req…
SegmentationSemantic SegmentationSuperpixelsRethinking Unsupervised Neural Superpixel Segmentation
Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied. Essentially, such methods generate superpixels by convolutional neural network (CNN) employed on a single image, and s…
SegmentationSuperpixelsAdaptive Superpixel for Active Learning in Semantic Segmentation
Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dom…
Active LearningSegmentationSemantic SegmentationSuperpixelsImproved Robustness from Biologically Inspired Sparse Contrast Representations
Deep neural networks surpass humans on many vision benchmarks, yet remain far less robust to distribution shifts such as illumination and weather changes. Existing approaches address this challenge by additional training…
Semantic SegmentationMSP : Refine Boundary Segmentation via Multiscale Superpixel
In this paper, we propose a simple but effective message passing method to improve the boundary quality for the semantic segmentation result. Inspired by the generated sharp edges of superpixel blocks, we employ superpix…
Scene ParsingSegmentationSemantic Segmentation