Exploration of image level classification based on semantic segmentation

Abstract

Semantic segmentation is a fundamental computer vision task where an image is divided into segments, with each segment assigned a class label based on its visual content. The objective is to achieve a pixel-level understanding of the image, enhancing machines' ability to comprehend and interpret visual scenes. This technique finds utility across diverse domains such as autonomous driving, medical image analysis, scene comprehension, and image editing, among others. Traditional per-pixel classification methods often encounter challenges related to class imbalances within segmentation datasets. To address this, a novel approach has been proposed, leveraging human-provided hints or auxiliary training signals derived from contextual modeling in segmentation. Human-in-the-loop techniques are employed to validate subtasks, correcting segmentation errors and enhancing mean Intersection over Union (mIoU) metrics without the need for additional trained parameters.