What is Semantic Segmentation?

Semantic segmentation is a foundational technique in computer vision that focuses on classifying each pixel in an image into specific categories or classes, such as objects, parts of objects, or background regions. Unlike instance segmentation, which differentiates between individual object instances, semantic segmentation provides a holistic understanding of the image by segmenting it into meaningful semantic regions based on the content and context of the scene.

Workflow of Semantic Segmentation

1. Data Analysis: Analyze labeled training data to understand object classes and segmentation patterns.
2. Network Design: Create a semantic segmentation network with convolutional layers for feature extraction, contextual information integration, and upsampling layers for dense classification.
3. Training: Train the network using the annotated dataset to learn pixel-wise classification and optimize segmentation accuracy using loss functions like cross-entropy or Dice loss.
4. Inference: Deploy the trained model to process unseen images and generate segmentation masks by classifying each pixel into specific semantic categories.

Some of the Semantic Segmentation techniques are U-Net, FCN (Fully Convolutional Networks), DeepLab, PSPNet (Pyramid Scene Parsing Network) and SegNet.

Applications of Semantic Segmentation

• Scene Understanding: Semantic segmentation aids in understanding the content and context of complex scenes by identifying and categorizing various objects and regions within an image.
• Autonomous Driving: In autonomous vehicles, semantic segmentation enables scene perception by detecting and classifying objects like roads, pedestrians, vehicles, and obstacles to navigate safely.
• Medical Image Analysis: Semantic segmentation is crucial in medical imaging for identifying and segmenting anatomical structures or abnormalities, assisting in diagnosis and treatment planning.
• Video Surveillance: In video analytics systems, semantic segmentation facilitates object detection and tracking by segmenting and analyzing the motion and behavior of objects over time.
• Image Editing and Augmentation: Semantic segmentation powers advanced image editing and augmentation techniques by enabling precise selection and manipulation of specific objects or regions in the image.

Image segmentation task involves partitioning the image into many segments or regions based on color, intensity, texture or spatial proximity. In this article, we are going to understand semantic segmentation, instance segmentation and their key differences.