What is semantic segmentation?

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Semantic segmentation is a computer vision task where each pixel in an image is classified into a category, so that the image is divided into meaningful regions based on objects or classes. Unlike object detection (which draws bounding boxes) or image classification (which assigns one label per image), semantic segmentation provides pixel-level understanding.

🔹 Key Idea

• Input: An image (e.g., a street scene).

• Output: A segmentation map, where every pixel is assigned a label like road, car, pedestrian, building, sky.

Example:

• All pixels belonging to “cars” are colored red.

• All “roads” are colored gray.

• All “trees” are colored green.

🔹 How It Works

1. Feature Extraction

   • A convolutional neural network (CNN) extracts spatial and semantic features from the image.

2. Pixel Classification

   • Each pixel (or small region) is assigned a probability of belonging to different classes.

3. Segmentation Map Generation

   • The highest-probability class is chosen, forming a labeled output image.

🔹 Types of Segmentation

1. Semantic Segmentation – Classifies pixels into categories, but doesn’t distinguish between individual instances.

   • Example: Two cars next to each other → all labeled as “car,” no separation.

2. Instance Segmentation – Extends semantic segmentation by separating different objects of the same class.

   • Example: Car 1 vs Car 2.

3. Panoptic Segmentation – Combines both semantic and instance segmentation.

🔹 Applications

• Autonomous vehicles: Road, lane, pedestrian, and obstacle detection.

• Medical imaging: Identifying tumors, organs, or cells.

• Satellite imagery: Land use classification (water, forest, urban).

• Augmented reality: Object/background separation.

✅ In short: Semantic segmentation means labeling every pixel of an image with a class, enabling fine-grained visual understanding for tasks like self-driving cars, medical diagnosis, and scene interpretation.

Read more:

What is ImageNet dataset?

What are common applications of computer vision?

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