Semantic segmentation labeling is a process in computer vision and image analysis that involves labeling each pixel in an image with a corresponding class label. Unlike object detection, which identifies and localizes individual objects within an image, semantic segmentation assigns a label to every pixel, creating a dense prediction map that highlights different regions or objects of interest.
The goal of semantic segmentation is to partition an image into meaningful and coherent regions based on the semantic understanding of the scene. Each pixel is assigned a label representing the category or class it belongs to, such as person, car, road, tree, etc. This fine-grained labeling enables detailed analysis of the image, allowing for pixel-level understanding and accurate delineation of objects or regions.
Semantic segmentation labeling is essential in various applications, including autonomous driving, medical image analysis, scene understanding, and video surveillance. It aids in tasks like object recognition, scene understanding, instance segmentation, and visual understanding for artificial intelligence systems. By providing a detailed understanding of the image at the pixel level, semantic segmentation plays a crucial role in advancing computer vision applications.