Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation

TL;DR

This paper introduces a context-aware framework for high-resolution image segmentation that leverages neighboring patches to improve accuracy without increasing feature map size.

Contribution

It proposes a novel method to incorporate neighboring patch context, enhancing segmentation quality in high-resolution images compared to traditional patch-based approaches.

Findings

Significantly improved mean Intersection over Union

Higher overall accuracy in segmentation tasks

Effective context integration from neighboring patches

Abstract

High-resolution image segmentation remains challenging and error-prone due to the enormous size of intermediate feature maps. Conventional methods avoid this problem by using patch based approaches where each patch is segmented independently. However, independent patch segmentation induces errors, particularly at the patch boundary due to the lack of contextual information in very high-resolution images where the patch size is much smaller compared to the full image. To overcome these limitations, in this paper, we propose a novel framework to segment a particular patch by incorporating contextual information from its neighboring patches. This allows the segmentation network to see the target patch with a wider field of view without the need of larger feature maps. Comparative analysis from a number of experiments shows that our proposed framework is able to segment high resolution images with significantly improved mean Intersection over Union and overall accuracy.