Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Semantic segmentation is a crucial task in computer vision that focuses on labeling each pixel within an image by assigning it a specific class or category. This means that not only are distinct objects detected within the image, but every pixel is classified according to the object or background it belongs to. For instance, in a labeled image of a street scene, pixels representing cars, pedestrians, trees, and the road surface would each receive a corresponding label. This pixel-level classification is essential for applications such as autonomous driving, medical imaging, and any scenario where detailed understanding of the scene's structure is necessary.

The other options, while related to computer vision, do not accurately describe semantic segmentation. Identifying objects in images relates more to object detection, which focuses on finding and classifying whole objects rather than individual pixels. Summarizing text data refers to natural language processing, where the focus is on extracting and condensing information rather than image segmentation. Data augmentation involves techniques to artificially expand the diversity of your training set by creating modified versions of images but does not pertain directly to the task of semantic segmentation itself.