Image Recognition
Image recognition is the task of recognizing images and grouping or distinguishing them from one another by drawing a box around them. The field of study aimed at enabling machines with this ability is called computer vision. Being one of the computer vision (CV) tasks, image classification serves as the foundation for solving different CV problems.
In simple words, image recognition is a task wherein the machine (say computer), learns to see, think and recognize just as we humans do. Just like how we can recognize people we know, the machine learns to recognize images which it has information about.
Requirements of Image Recognition
Since the machine has to be trained to see like us, it has a set of predefined libraries which we can use whenever we are working with computer vision. These libraries help us to train the model more efficiently. This is the first and foremost requirement of an image recognition model.
Second, and the most important requirement is data. If the computer doesn’t have enough data , how can it help in detection?
Accurate data is a must for proper image recognition. A just born child can’t identify a cat or dog. This child has been trained over years to identify objects around him/her. Same goes with the machine, we need to give it enough data, with a tag for each object, and not just one image is enough for it to recognize accurately. For example, if you want to build a cat detection model, you will have to have a dataset, which has multiple images of a cat, from different angles, as well, so that when we have a image that needs to be recognized, the machine can do so perfectly.
You might now realize why your phone asks you to scan your finger multiple times, while setting a password. The phone stores all these images and makes use of it to recognize our fingerprint. It is similar to how we teach small children , what is an apple, and what is a cow. Only once the brain learns, can humans recognize these things. Same goes with the machines, once it is trained through the dataset, it can help recognize images and also keep learning along the way.
Advantages:
- Logo detection in social media analytics:
It’s not only used for measuring brand awareness. Businesses are using logo detection to calculate Region of Interest (ROI) from sponsoring sports events or to define whether their logo was misused.
- Medical image analysis:
The system analyzes medical images and then combines this insight with information from the patient’s medical records, and presents findings that radiologists can take into account when planning treatment.
- Facial recognition to improve airport experience:
Facial recognition is becoming mainstream among airlines that use it to enhance boarding and check-in. There are two main directions of these upgrades: to follow the trends for self-service and this bio metric technology and make the airport experience safer and faster. The fewer steps both passengers and staff must make to proceed with pre-flight routines, the better.
And many more… So, even if we can use our eyes to see, image detection is something that can and will bring wonders in the already amazing world that we are in !
