Image Classification
One of the main responsibilities of computer vision is image classification. The primary goal is to assign a predefined label or category to an input image by identifying the main content of the specific image. The computer system predicts which class or category the main image content belongs to. Image classification mainly deals with a single object. For example, an image classification model could be trained to identify and label an image, if the image contains a cat, a dog, a car, a human or a specific object.
Explained below are different types of image classification, and how image classification works.
Types of Image Classification:
There are two main types of image classification for categorizing images into predefined classes:
- Single-Label Classification: In single-label classification, each image is assigned to one single category, where the goal is to predict one label per image. For example, classifying an image as containing a cat or a dog.
- Multi-Label Classification: Multiple-Label classification involves assigning multiple labels to an image which has multiple objects. For example, an image might contain a cat, a dog and a tree and the image classification recognizes all these objects and labels them.
Computer Vision Tasks
Computer vision is a branch of artificial intelligence that helps computers understand and analyze visual data from digital images, videos, and similar visual inputs. Using digital visual data obtained from various sources, we can teach computers to detect and interpret visual objects. It also plays a critical role in areas such as image recognition and object detection. There are many different tasks that computer vision can perform. In this article, we will discuss computer vision tasks in detail.
Table of Content
- What are computer vision tasks?
- Image Classification
- Object Detection
- Image Segmentation
- Face and Person Recognition
- Edge Detection
- Image Restoration
- Feature Matching
- Scene Reconstruction
- Video Motion Analysis
- Conclusion: