Humans can recognize and discern various features of objects – alive or inanimate. Since the invention of computers, humans have wished for such recognition abilities in their computers.
But this was not possible till 2001.
2001 saw the beginning of an aspect of technology which we would later term as ‘Image Recognition‘.
That year, Paul Viola and Michael Jones designed a functional algorithm that was able to detect faces on a webcam. Their demo was a significant advancement in the field of technology after years of stagnancy.
What Is Image Recognition?
Image recognition is the ability of a program to recognize the variables in images (objects, people, places, writings and much more).
Image recognition is a complex process for machines to carry out.
Image recognition is a sub-unit of computer vision – a branch of computer science. It comprises a set of techniques that assist in:
- Interpretation of images for decision-making
Image Recognition and Deep Learning
Every decade presents us with some very innovative and credible ideas that make a breakthrough in the field of science. Deep Learning is this decade’s ‘that idea’.
Image recognition was not always convenient because researchers depended on their knowledge to extract and demonstrate image features using an algorithm.
When it was paired with Deep Learning, it provided far superior outputs. There are many types of Deep Learning approaches that are useful for image recognition.
Nonetheless, convolution neural networks (CNN) produce the best solution using the unique work principle. The CNN method consists of:
- Convolution layer
- Activation function
- Pooling layer
During this phase (training phase), the different levels of factors are divided into three separate parts:
- Low-level (color, lines, and contrast)
- Mid-level (edges and corners)
- High level (class and specific forms or sections)
CNN helps decrease the computation power requirement while allowing the treatment of large size images effectively.
Image Recognition, Object Localization and Image Detection
Often confused with each other, these are three very different.
Computer systems use image recognition to understand what an image comprises. Object localization helps specify the location of a particular object in an image. Image detection deals with identifying the location of multiple objects in an image.
Backbone Of the System
The three main steps that form the backbone of the image recognition system are as follows:
- Training Data – Training data is required by a neural network from an acquired dataset to create insight into how certain classes look.
- Training of neural networks – Here comes the Deep Learning aspect of creating an image recognition model. The neural network algorithm receives the images from the dataset.
- Ai Model Testing – After the initial run, the trained model needs to be tested with images that are not part of the initial training dataset.
Best Image Recognition Algorithms
Here are some of the best image recognition algorithms:
- Faster region-based CNN (RCNN) – Faster R-CNN is the best performer in the R-CNN family. It is an object detection model that improves the Fast R-CNN by utilizing an RPN. Faster R-CNN can process a single image in 200ms whereas, Fast R-CNN takes about 2 seconds.
- Single Shot MultiBox Detector (SSD) – Single-stage object detector that separates the output space of bounding boxes and places them in default boxes keeping in mind the various aspects. SSDs are very accurate, flexible and easy to train. An SSD can process a single image in 125ms.
- You Only Look Once (YOLO) – True to its name, this algorithm only processes a frame once using fixed grids. It then determines if a certain grid contains an image or not.
Despite all of these being Deep Learning algorithms, their basic approach towards different classes of the object varies largely.
Image recognition has its own set of challenges, such as:
- Viewpoint differences – When images with objects aligned in different directions are given to the image recognition system, it fails to comprehend the image’s different alignment. This makes it one of the biggest challenges.
- Variations of scale – The changes in the dimensions of an image can provide inaccurate results. Size variation plays a vital role in the classification of objects in images.
- Shapes and sizes – In the real world, the objects and images change, resulting in inaccuracy. The system learns from an image how a specific object should look like. We all know that objects come in various sizes and shapes that can pose a problem in image recognition.
- Group variations – Image recognition categorizes the major elements in different classes. But what about the minor ones? Some items that differ are still placed within the same class.
- Obstruction – If any object is placed in a way that blocks a proper and complete view of the image, it can result in incomplete information being fed to the system.
Some Futuristic Uses
Technology has the power to change the world and, with image recognition, that power is in our hands.
Here are a few of many things that we can implement in the real-world:
- Improved augmented reality experience – Gaming is an expanding field. The gaming sector has started using image recognition technology along with augmented reality to upgrade the user experience. Image recognition helps in creating realistic backgrounds and characters.
- Improved educational quality – Image recognition is used by educational sectors. It provides an easy learning solution for students with learning disabilities and has changed the course of education.
- Improved medical imagery – Technology is not limited to education. With image recognition in the medical field, detecting severe illnesses that go undetected will be easier.
- Improving driverless car experience – The future of the automotive industry is right here. Image recognition technology plays a part in enabling speed prediction of the vehicle. What’s impressive is that researchers are working to create AI that allows the car to see in the dark.
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