An Essential Guide to image recognition gives you a complete understanding of what AI is, what it can do, and how it works. The guide contains articles on machine learning, computer vision, natural language processing, and algorithms. Knowing all these topics helps you in a clear understanding of image recognition.
Training a computer to see the world as humans do, requires computer vision and image recognition. It’s the same as a newborn baby. They can see everything but requires training to understand. Through proper training, they could understand what the object is.
Granting a camera to the machine doesn’t give it a sight and awareness.Arbab Ali
What is Image Recognition?
Image recognition is the analysis of pixel and pattern of an image to recognize as a particular object. Image recognition identifies objects, places, writing, and actions in images. Camera and artificial intelligence are combined to achieve excellent results in image recognition technology. Image recognition resolves machine-based visual tasks. The tasks include labeling images by meta-tags, performing image content search, guiding autonomous robots -band self-driving cars.
The human brain can understand the images easily. But when this comes to machine, it’s quite hard for them. We gave the input to machines by a camera. However, they have no power to understand what’s happening inside the images. Our brain has the marvelous power to recognize the happening around us. For machines, it requires deep learning, machine learning, and computer vision to generate output.
Evolution in the neural network makes this process even more accessible. Image recognition algorithms function by using qualified 3D models, pattern detection, and getting a view from different angles using edge detection. Models require much data to become mature. Reinforcement training makes the machine to be able to make decisions based on right and wrong situation.
Current and future applications of image recognition include smart libraries, media interaction, targeted advertising, accessibility for visually impaired, and enhanced research capabilities. Tech giants like Google, Facebook, Microsoft, Apple and Pinterest companies are vastly investing resources and research into image recognition and related applications. Privacy concerns over image recognition arise as these companies pull a large amount of data from user photos that are uploaded platforms.
Image Recognition vs Computer Vision
Image processing is a subset of computer vision. Computer vision uses image processing algorithms to simulate humans. Image processing enhances images, while computer vision detects objects for automation. Computer vision lets the computer to mimic the human vision and take action. For example, the CV can detect cancer tumors from CT scan images. CV can detect patterns from data that is not possible for a radiologist to predict. Image recognition gives a machine the ability to interpret the input through computer vision and Convolutional Neural Networks.
Learn more about how deep learning advances are boosting computer vision.
Image Recognition Examples
- Google Reverse Image Search enables you to search for a similar image on the web. On upload, it usually searches for the images in their database. Image having approximately the same histogram is presented to the searcher.
- Price Comparison like Google Shopper provides the flexibility to compare the product price in the nearby stores. Computer vision is what powers a bar code scanner’s ability to “see” a bunch of stripes.
- Self-Driving cars use computer vision and image recognition to detect road signs, analyze automotive, and people walking on the road. Based on the condition, it makes a decision. Future technology is waiting for the 5th level of self-driving cars.
- eBay app lets you search for the items by the click of your camera.
- Brain prints in AI. Just like face unlocks and fingerprint unlock. It is such a new concept. When humans see an image, different types of waves generate from their minds. That patterns are utilized to enable security features.
- Facebook AI knows a lot about our photos.
- Apple’s Face ID can clearly recognize its owner.
- Neural networks can turn black photos to bright images.
- An app like Not Hotdog makes a decision using computer vision and image recognition. Training a neural network to perform image recognition is more complicated. Rather than training a child to recognize the images. Computer vision gives it a sight while image recognition gives it an understanding.
Image recognition is the creation of a neural network for image pixel processing. Networks are presented with many images to learn from them. For example, we have a vast number of images of cars. The machine processes raw visual inputs, and our AI model learns from them. On training completion, if we gave an image of some cars to it. It can easily compare and recognize whether an input image is of a car or not.
Our biological neural networks are pretty good at interpreting visual information even if the Image we’re processing doesn’t look exactly how we expect from it. It’s easy enough to make a computer recognize a specific image, like a QR code, but they stuck at recognizing things in states they don’t expect — enter image recognition. Researchers feed these networks as many pre-labelled images as they can, in order to “teach” them how to recognize similar images.
Power of Image Recognition Tools
Before reading how image recognition works? It’s a good practice to check some of the tools that offer image recognition. After using analyze the results and learn how image recognition works. Most of the beginners want to test image recognition to know how it works to produce results. No one wants to write a bulk line of code to test and debug it.
So, here premade image recognition tools help like a low hanging fruit. They can recognize, analyze, and interpret images. I prefer to use Clarifai as it’s free and easy to use. Below we learn how the Clarifai generates results from an image. Here is how the generated output from the picture of a car.
How Does Image Recognition Work?
Now, its time to dig dive into understanding how image recognition works? How we train an image that can understand the difference between images? Image recognition works similarly, like leaning machine modeling. So, we have compiled and gave a brief of them.
Step 1: Pixel feature extraction
In the very first step, features are extracted from an image. An image is made of pixels. A set of numbers represents pixels. The range of these numbers is called the bit depth or color depth. Bit depth shows the maximum number of colors that are used by an image. In an 8-bit greyscale image, each pixel has one value that ranges from 0 to 255. Most images today use 24-bit color or higher. An RGB color image means Image is the combination of red, green, and blue.
Each of the colors ranges from 0 to 255. This RGB color generator shows how RGB can generate any color. So, a pixel contains a set of three values RGB (102, 255, 102) refers to color #66ff66. An image 800 pixel wide, 600 pixels high has 800 x 600 = 480,000 pixels = 0.48 megapixels (“megapixel” is 1 million pixels). An image with a resolution of 1024×768 is a grid with 1,024 columns and 768 rows, which therefore contains 1,024 × 768 = 0.78 megapixels.
Step 2: Prepare labeled images to train the model.
In the second stage, a supervised machine learning technique trains the model. Here the model is fed with different labeled images that fit in diverse categories. Thousands of features are extracted from the images with the help of labels. The more images we use for each category, the more chances are: our model can efficiently understand the Image. Proper training helps in better Recognition of an image. Here we already know the category of an image to use them for training the model.
Step 3: Train the model for categorizing
In the third stage, the model is trained with pre-labeled images—the vast networks in the middle act as a giant filter. The images in their extracted forms enter the input side. Pixel features to match with a vast database, and finally, labels generated at the output side. That’s how we train the neural networks. Such that an image with its features coming from the input matches the label in the right.
Step 4: Predicting an image from categories
On completion of model training, it can recognize an unknown image. A new image recognizes as a dog image. Notice that the new Image goes through the pixel feature extraction process. Ai system relies on computer vision that process visual information. Image recognition is essential for robots that need accurate Recognition and categorization.