I want to share how to build an image recognition software? Why? Because most of us are reliant on modern technology. On top of that, it seems all of us initially using the form of Image Recognition software. But before that, lets tackle a bit of information about image recognition.
The Idea Of Image Recognition
Image recognition is known as computer vision. Because it allows the application using specific deep learning algorithms, so it can understand image or video. Also, image recognition can identify objects, places, writing, etc.
Therefore, image recognition is using artificial intelligence and deep learning. Because it can easily imitate the human way.
Furthermore, image recognition AI is part of computer vision that has machine learning as a part of artificial intelligence and signal processing. In other words, image recognition is a part of the three.
For example, an image recognition software should not use a synonymously to signal process. Therefore it can be considered a part of the large domain of artificial intelligence and computer vision.
Okay, let’s take closer and show you what the four concepts mean.
Image Recognition
So an image is the key input and output element. Therefore the image recognition is design to understand the visual representation of a regular image.
In other words, the software is train to get a lot of useful information, and also it performs an important role to provide an answer to a question like what is the image. So this is how the term of image recognition is usually understood.
Signal Processing
Input is not only an image but also various signals like sounds and biological measurements. Therefore these signals are useful when it comes to voice recognition as well as for different applications like facial detection.
So signal processing is has a wider field than image identification technology and mixes with deep learning. Also, it capable of discovering patterns and relationships that, until now have unobserved.
Computer Vision
It is a scientific discipline that has a concern with building artificial systems that has receiving information from such input sources as images, videos, or multi-dimensional hyperspectral data.
Therefore, the computer vision process involves techniques such as:
- Face detection
- Segmentation
- Tracking
- Pose estimation
- Localization
- Mapping
- Object recognition
So the data are processed by the application programming interfaces.
Machine Learning
It is an umbrella term for all of the above concepts. Why? Because machine learning is cover image recognition, signal processing, and also computer vision.
Build Image Recognition Software
There two different methods of building image recognition software. Supervised learning and also unsupervised learning.
So using these two methods it can perform to detect an image. Therefore what is different between these two methods?
Supervised learning is a process that uses to recognize if a particular image is in a certain category. Then it will compare to the one in the category that has already been detected.
On the other hand, unsupervised learning is also a process to use when determining if an image is in a category by itself. So the neural networks are complex methods that design to allow for the classification and tracking of an image.
This is the multi-step processing for building image recognition software:
- The original image detection
- Analysis and classification of the data
- Reinforcement learning
- The AI training process
- Monitoring and replaying of the training process
