Semantic Segmentation vs Instance Segmentation
In this section, we are going to cover the key differences between the segmentation techniques.
| Criteria | Instance Segmentation | Semantic Segmentation |
|---|---|---|
| Definition | Identifies and delineates individual object instances at the pixel level. | Classifies each pixel into specific categories or classes without distinguishing between instances. |
| Objective | Provides detailed object-level segmentation by distinguishing between different instances of the same category. | Offers a holistic understanding by segmenting an image into broad semantic regions based on object categories. |
| Detail Level | Operates at a granular level, differentiating between individual object instances within the same category. | Provides a broader segmentation, grouping pixels into general object categories. |
| Differentiation Ability | Can distinguish between different instances of the same category by assigning unique labels or colors. | Cannot differentiate between individual instances of the same category, all pixels of the same class are grouped together. |
| Approach | Combines principles of object detection, semantic segmentation, and pixel-wise labeling. | Typically involves sequential processes such as feature extraction, pixel-wise classification, and object localization. |
| Output | Produces segmentation masks that differentiate between individual object instances. | Generates segmentation maps or masks that classify pixels into specific semantic categories. |
| Complexity | More complex due to the need for precise object instance differentiation. | Generally simpler, focusing on broad object categorization without detailed instance differentiation. |
| Applications | Ideal for tasks requiring accurate object detection, tracking, and recognition in complex scenes. | Commonly used in applications where a general understanding of the image content is sufficient, such as scene understanding and object classification. |
| Datasets | Examples include LiDAR Bonnetal Dataset, HRSID, SSDD, Pascal SBD, iSAID, etc. | Examples include Stanford Background Dataset, Microsoft COCO Dataset, MSRC Dataset, KITTI Dataset, Microsoft AirSim Dataset, etc. |
Semantic Segmentation vs Instance Segmentation
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.
Contact Us