Image segmentation is a computer vision technique that divides an image into meaningful regions to help identify and analyze objects more effectively. It simplifies visual data by assigning labels to pixels based on their characteristics.
- Semantic segmentation classifies each pixel into a category without distinguishing between individual objects.
- Instance segmentation separates each object individually, even if they belong to the same class.
Semantic Segmentation
Semantic segmentation is a computer vision technique that divides an image into meaningful regions by assigning a class label to every pixel. It helps in understanding the overall scene by grouping pixels that belong to the same category, such as objects, parts of objects, or background.
Workflow
- Data Analysis: Understand labeled datasets to identify object classes and segmentation patterns.
- Network Design: Build a CNN-based model with feature extraction, context learning, and upsampling layers for pixel-wise prediction.
- Training: Train the model using annotated images and optimize it using loss functions like cross-entropy or Dice loss.
- Inference: Apply the trained model to unseen images to generate pixel-level segmentation maps.
Some popular semantic segmentation models include U-Net, FCN (Fully Convolutional Networks), DeepLab, PSPNet and SegNet.
Instance Segmentation
Instance segmentation is a computer vision technique that detects and separates each object in an image at the pixel level. It combines object detection and segmentation to outline individual objects precisely. It assigns a unique label to each object, even within the same class, enabling fine-grained separation of multiple instances in a scene.
Workflow
- Object Detection: The model identifies objects in the image by predicting bounding boxes and their corresponding class labels.
- Bounding Box Refinement: The predicted boxes are refined to improve the accuracy of object localization.
- Mask Generation: A segmentation mask is generated for each object within its bounding box to separate it from the background.
- Instance Labeling: Each object is assigned a unique label, and all masks are combined to form the final instance segmentation output.
Common instance segmentation models include Mask R-CNN, Faster R-CNN with Mask Branch, Cascade Mask R-CNN, SOLO and YOLACT.
Key Differences
| Criteria | Instance Segmentation | Semantic Segmentation |
|---|---|---|
| Definition | Identifies and separates each object instance at pixel level | Assigns a class label to each pixel without separating instances |
| Objective | Distinguishes individual objects within the same category | Provides overall scene understanding by grouping pixels into categories |
| Detail Level | Fine-grained, object-level segmentation | Coarse-grained, category-level segmentation |
| Differentiation | Separates multiple objects of the same class | Does not distinguish between objects of the same class |
| Approach | Combines object detection and pixel-wise segmentation | Uses pixel-wise classification of the image |
| Output | Separate mask for each object instance | Single segmentation map per class |
| Complexity | Higher due to instance-level separation | Lower as it focuses only on class-level grouping |
| Use Cases | Object tracking, detection in crowded scenes | Scene understanding, background–foreground labeling |