What is Image Classification?
Image classification refers to the process of assigning a label to an image based on its visual content. The primary goal is to identify the objects or patterns within an image and categorize it into one or more predefined classes. For example, an image classification system can determine whether a photograph contains a cat, a dog, a tree, or another object.
Table of Content
- What is Image Classification?
- Types of Image Classification
- 1. Binary Classification
- Binary classification involves classifying images into one of two categories. For example, determining whether an image contains a cat or not. This is the simplest form of image classification.
- 2. Multiclass Classification
- 3. Multilabel Classification
- 4. Hierarchical Classification
- 5. Fine-Grained Classification
- 6. Zero-Shot Classification
- 7. Few-Shot Classification
- Image classification vs. object detection
- How Image Classification Works?
- Data Collection and Preprocessing:
- Feature Extraction:
- Model Training:
- Model Evaluation and Testing:
- Deployment:
- Algorithms and models of Image Classification
- Applications of Image Classification
- Challenges in Image Classification
What is Image Classification?
In today’s digital era, where visual data is abundantly generated and consumed, image classification emerges as a cornerstone of computer vision. It enables machines to interpret and categorize visual information, a task that is pivotal for numerous applications, from enhancing medical diagnostics to powering autonomous vehicles. Understanding image classification, its working mechanisms, and its applications can provide a glimpse into the vast potential of artificial intelligence (AI) in transforming our world.