Imagine someone showing you an animal that you’ve never seen before in your life. If you look closely, you will surely recognize it the next time you see it. Simple, right? Not for computers. Artificial intelligence experts have been teaching computers how to recognize objects for years. And although they are getting better at it, even the most advanced programs still don’t work perfectly. Why? We will find the answer to this question by taking a closer look at what object recognition is all about.
What is Object Recognition and How Does it Work?
Object Recognition involves using artificial intelligence to recognize, identify, and position objects in images and videos.
To do this, high-quality data needs to be provided to the computer algorithms. This way, the programs learn the characteristics of individual objects. Then, if the learning process is successful, the algorithms will be able to recognize specific objects in images and videos.
In theory, this seems simple. After all, the human brain performs such operations automatically. However, artificial intelligence doesn’t find it as easy, and the quality of image recognition depends on the quantity and quality of the previously provided data.
Methods and Applications of Object Recognition
The main methods of image recognition include:
- classification
- tagging
- detection
- segmentation
Object classification involves the algorithm recognizing that there is only one object, thereby ignoring all others. In the case of tagging, the algorithm identifies multiple objects in a given image. Unlike classification, tagging aims to indicate the class of all objects in the image. Object detection is based on image processing, enabling the detection of specific classes of objects in images and videos. On the other hand, segmentation is a process that not only determines the location of an object but also its precise shape (as the algorithm can assign each pixel of the image to a specific category).
One of the most important applications of object recognition is in the automotive industry and autonomous vehicle technology. Through image recognition, such a car can identify road signs and differentiate pedestrians from stationary objects.
Moreover, “image recognition” is used in cameras for facial or iris recognition to identify individuals. In retail, intelligent cameras detect customers and track their behavior during shopping. This enables brands to better design their stores, especially the layout of products on shelves.
Object Recognition and Other Technologies
Object recognition is sometimes confused with other technologies such as object detection and object tagging. How do they differ from each other?
As the name suggests, object detection involves detecting a specific class of objects and locating them in an image. Only after the object is detected, image recognition programs come into play to determine what that object is. On the other hand, object tagging is the process of assigning specific characteristics to objects that the algorithm is aware of.
The differences between these three technologies can be illustrated as follows:
- Object detection: There is an object.
- Object recognition: This object is 95% likely a cat.
- Object tagging: This is a Scottish Fold cat with a 95% certainty.
Object recognition techniques
Object recognition is based on various types of machine learning. In the case of deep learning, algorithms are used to automatically learn distinctive features of a given object, which can be used to identify it. For example, a program learns to identify the differences between dogs and cats by analyzing thousands of images and distinguishing the features that make cats different from dogs.
On the other hand, to utilize machine learning other than deep learning for image recognition, a dataset of images or videos is required, where relevant features of each image are annotated. These features are then added to the machine learning model, which separates them into separate categories, using this information for analysis and classification of new objects.
Using such machine learning for object recognition provides greater flexibility in choosing the best combination of features and categories for learning. Additionally, accurate results can be obtained even with minimal input data.
However, deep learning requires powerful graphics processing units and a massive amount of annotated data. If you don’t have both of these things, it is better to choose machine learning that does not rely on deep learning.
Advanced techniques and commonly used methods in object recognition
Some other techniques used for object recognition include:
- Template matching: This technique involves comparing objects to templates, enabling the recognition of characters, letters, numbers, and objects.
- Color-based recognition: This technique is used when color is a distinctive characteristic of objects. For example, it allows distinguishing an orange from an apple based on the knowledge that oranges are exclusively orange, while apples can be red, green, or yellow.
- Shape-based recognition: This technique involves differentiating objects from one another based on their characteristic shapes.
Summary and sources
What comes naturally to humans poses a significant challenge for computer programs. However, with access to increasingly affordable and powerful computational resources, as well as high-quality images and videos, algorithms are becoming more adept at object recognition. As a result, artificial intelligence is already assisting humans in areas where computer vision can accurately analyze and interpret incoming data.