AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is semantic segmentation in computer vision?
⚠ Common exam trap
Test-takers frequently confuse semantic segmentation with object detection (Option A) because both involve identifying objects, but segmentation requires pixel-level precision rather than bounding boxes.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Classifying each pixel in an image into a semantic category
Semantic segmentation is a computer vision task that assigns a class label to every single pixel in an image, effectively partitioning the image into regions that correspond to different semantic categories (e.g., road, car, pedestrian). This is distinct from object detection, which only provides bounding boxes around objects, and from image captioning or OCR, which operate at a higher or different level of abstraction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Detecting the boundaries of objects using rectangular boxes
Why it's wrong here
Object detection identifies instances and localizes each one with an axis-aligned rectangular bounding box, typically outputting class scores and box coordinates. Semantic segmentation outputs a pixel-level class label for every pixel, respecting object boundaries rather than approximating them with rectangles. Because bounding boxes can include unrelated background pixels, they are a coarser representation than dense semantic labels.
- ✓
Classifying each pixel in an image into a semantic category
Why this is correct
Semantic segmentation is a dense prediction task in which each pixel is assigned a class label from a fixed set of semantic categories, such as person, car, or background. Models like fully convolutional networks, U-Net, or DeepLab use spatial features and upsampling to produce an output map with the same resolution as the input. This pixel-wise classification gives a detailed scene understanding that is more granular than object-level or block-level annotations.
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Generating natural language descriptions of images
Why it's wrong here
Generating natural language descriptions of images is image captioning, an image-to-text task that produces a sentence-level summary using encoder-decoder architectures. Semantic segmentation, in contrast, makes per-pixel category predictions across the entire image plane and never generates textual tokens. The output space is fundamentally different: a dense classification map rather than a variable-length natural language sequence.
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Extracting text from images using OCR
Why it's wrong here
OCR locates and transcribes machine-printed or handwritten text from images into machine-readable characters or words, often returning detected regions as polygons or bounding boxes. Semantic segmentation classifies every pixel into predefined visual categories, such as road, sky, or vegetation, rather than recognizing textual characters. The primary goal differs: OCR reads symbolic content, while segmentation understands scene structure at the pixel level.
Go deeper
Related to this question
Learn chapter
What is Computer Vision?
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
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