The latest report on mobile image recognition tool usage worldwide published by Statista in September 2021 claims that in the first quarter of 2021, Brazil recorded the highest reach of mobile image recognition tools, with more than 50 percent of its Internet population reporting using such tools. Colombia followed in second place, with 49 percent of Internet users reporting using image recognition tools on mobile on a monthly basis.
Image recognition is quite like a fingerprint.
Interestingly, it can pick a person or an object out of a crowd, unlike the vending machine at work that can’t recognize a dollar with a bent corner.
Although humans have a high image processing power and the ability to precisely recognize objects, places, people and text, they have a limitation when it comes to recognizing images from a large set of data.
Thanks to the advances in Machine Learning (ML) and Artificial Intelligence (AI), computers and smartphones these days can imitate the potential of the human brain.
It is no surprise that Artificial Intelligence today can actually make machines behave like humans and enable them to recognize different images, from a large data set, without human intervention.
From complex surveillance software to self-driving cars, iris recognition improvement, optimized medical imagery, and other image recognition tools, the image recognition technology has diverse use cases today.
The app stores have a diverse range of apps that deploy image recognition technology at the backend.
By developing precise and flexible image recognition algorithms, you can enable users to identify images, text, videos, and objects through your app.
Let’s find out how an image recognition app works, the key features to include in it, the best practices for creating your own image recognition app, and the technologies to use when doing so.
How an image recognition app works
Did you know that Facebook can now perform face recognition at 98% accuracy which is comparable to the image recognition ability of humans?
Its powerful ability to classify images is what leads to this image recognition accuracy.
Classification is pattern matching with data. Think of images as data in the form of 2-dimensional matrices. Image recognition is the process of classifying data (images) into one category out of many.
Optical Character Recognition (OCR) is one of the most common examples of this that converts images of text into machine-encoded text.
- Detection of images is performed using two different computational methods, referred to as neural network methods:
- 01. Classification or supervised learning:
This method determines if a particular image is in a certain category, and then it is compared with the ones in the category that have already been detected. It uses a machine learning algorithm to estimate a feature in the image called an important characteristic which it uses to make a prediction about whether an image is likely to be of interest to a given user.
- 02. Unsupervised learning:
This method determines if an image is in a category by itself.
- Metadata classifies images in different tags called information classes that the image recognition engine uses to extract information such as size, color, format, and format of borders to understand the meaning of the image.
Pattern recognition in image processing is a multi-step process that includes:
An image recognition software app will most probably use a combination of supervised and unsupervised algorithms.
Key features to include in an image recognition app
The list of user and admin features that should be infused for IR driven app
- Upload photo option
- Barcode and QR code scanning
- Push notifications
- Real-time image detection
- Manage profile
- Document management
- Mark to Favourite
- Filter usage to find exact matches
- Add to cart
- Buy now
- Automatic comparison from database
- Share your screenshot with social media
- Filter category control
- Manage to filter
- Reports and analytics gathering control
- Sharing management
- Customer management
- Manage database systems
Top practices for building an image recognition app
The major steps in the image recognition process are gathering and organising data, building a predictive model, and using it to recognize images.
- 1. Collect data:
The human eye perceives an image as a set of signals which are processed by the visual cortex in the brain. The vivid experience of a scene, associated with objects recorded in our memory is the result of this processing.Image recognition tries to mimic this processing by the visual cortex in the brain.The image recognition algorithm perceives an image as either a raster or a vector image, transforms the geometric encoding into constructs depicting physical features, and finally analyzes them.
- 02. Organise data:
This step involves image classification and feature extraction. Images often have a significant variation in RGB pixel values, but by simplifying them (by running an edge detection), you can easily extract important features from an image while throwing away non-essential information.Some well-known feature descriptor techniques are Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), and Speeded Up Robust Feature (SURF), among others.
- 03. Prepare data- Build a good dataset:
Collecting and organising data alone is not enough if you want to train your AI system to recognize objects and concepts.You need to annotate it to be able to do so. For this, you need to label images, which means telling the system what is and what is not present in an image.Tags, bounding boxes, and lines and polygons are some common labelling methods of annotating your images.Quick hacks to overcome challenges in step 1 and 2:
- 4. Build a predictive data model- Train your image recognition model:
While the above two steps would take up significant effort, this step is a lot easier. The collected, organised and prepared dataset is fed into the image recognition model to recognize images.All you need to do is train a classifier that can take the measurements from a new test image and find the closest match with the search query in the database. Running this classifier takes milliseconds.The major challenges in building an image recognition model are
- Hardware processing power, and
- Cleansing of input data
- 05. Deploy your model- put it into production:
Your model is trained. It’s time to put it into production.The choice of hardware on which your image recognition system will run depends on the tasks you want it to perform and the pace at which you expect it to work.If the performance and speed of your image recognition model are a priority, your best choice is to invest in a GPU (Graphics Processing Unit).However, it’s important to note that they’re far more expensive and energy-consuming than a CPU (Central Processing Unit) which would be ideal if speed isn’t a critical factor for you
- 06. Design the User Interface:
Once the image recognition model is deployed, you need to work on the user interface. Keep your user interface simple to read and interpret.If you are designing an image recognition application for Android, you can design the user interface using Kotlin or Flutter.If you’re designing a web image recognition application, Flask or Django would be your best bet since they let you integrate the Tensorflow library and lets you use the model weights for making the right kind of prediction on the input image.
Image recognition app development has already started reducing human work in many spheres.
For instance, worldwide burger restaurant Caliburger is using facial recognition to identify repeat customers and remember their previous orders. And Alibaba’s Fliggy and hotel technology company Shiji is behind a facial recognition system for hotel check-ins.
Do you have an image recognition app for your own business in mind? Or do you want to integrate image recognition into your business solution?
Jellyfish Technologies is a leading digital agency that works with the latest AI, ML, and image recognition algorithms to develop apps that mimic human brains and recognize objects, text, videos, people, and more from large and complex data sets.