Image segmentation vs. object detection vs. image classification
The comparison between Image segmentation, object detection and image classification are as follows:
Aspect |
Image Classification |
Object Detection |
Image Segmentation |
---|---|---|---|
Purpose |
Assign a label or category to the whole image |
Identifies and locates multiple objects |
Divide the image into meaningful regions |
Output |
Single label or category |
Bounding boxes around detected objects |
Pixel-wise segmentation masks |
Focus |
High-level classification of the entire image |
Detection of objects with localization |
Detailed segmentation of objects and background |
Complexity |
Simpler and faster |
Moderate complexity |
Typically more complex and computationally intensive |
Applications |
Image search, content filtering |
Self-driving cars, facial recognition |
Medical imaging, autonomous robots |
Examples |
“Cat” for a picture of a cat |
Cars & pedestrians in a traffic scene |
Separating tumor from healthy tissue in an X-ray |
Explain Image Segmentation : Techniques and Applications
Image segmentation is one of the key computer vision tasks, It separates objects, boundaries, or structures within the image for more meaningful analysis. Image segmentation plays an important role in extracting meaningful information from images, enabling computers to perceive and understand visual data in a manner that humans understand, view, and perceive. In this article let us discuss in detail image segmentation, types of image segmentation, how image segmentation is done, and its use cases in different domains.
Table of Content
- What is Image Segmentation?
- Why do we need Image Segmentation?
- Image segmentation vs. object detection vs. image classification
- Semantic Classes in Image Segmentation: Things and Stuff.
- Semantic segmentation
- Instance segmentation
- Panoptic segmentation
- Traditional image segmentation techniques
- Deep learning image segmentation models
- Applications of Image segmentation
- Conclusion: