Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation
This addresses segmentation errors in high-resolution images for applications like medical imaging or satellite analysis, though it is incremental as it builds on patch-based methods.
The paper tackles the problem of high-resolution image segmentation by proposing a framework that incorporates contextual information from neighboring patches to reduce errors at patch boundaries, resulting in significantly improved mean Intersection over Union and overall accuracy.
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.