Why image classification matters
Image recognition can help us to speed up tedious tasks and leverage automation to structure and organize information. This technique identifies objects or scenes in images, then processes and classifies the information extracted to make decisions as part of a larger system. Image recognition is a fundamental component for solving many computer vision-based AI problems and is the main driver in deep learning applications such as:
- Image classification: the process of identifying and categorizing various details in an image.
- Visual inspection: the process of inspecting thousands of parts for defects on an assembly line.
- Automated driving: the process of identifying road signs, visualizing sensor data, and detecting lanes, vehicles, and pedestrians.
- Robotics: the process of identifying objects and enhancing autonomous navigation.
Image recognition vs. object detection
Image recognition and object detection are similar techniques that complement one another. Image recognition identifies an object or scene in an image or video and assigns a single high-level label. Object detection identifies each and every object in the image or video and finds instances and locations of these objects.
Why use deep learning techniques for image recognition?
There are many methods and techniques to identify and categorize images and videos. However, when you are facing a complex problem, you need a complex solution, and this is where deep learning comes into the picture.
Deep learning techniques provide highly accurate and robust results. A deep learning approach works best with a large amount of training data and, for that reason, often involves the use of a pre-trained convolutional neural network (“CNN” or “ConvNet”) to train an image category classifier to identify selected features in images and videos automatically.
A deep learning workflow for image recognition includes:
- Accessing and exploration of data
- Labeling and preprocessing of data
- Developing of predictive modules
- Integration of models into your system.
Deep learning techniques are not an easy thing to understand, but you can get started and learn more about the technology by getting familiar with these simple examples: