What is image recognition and how does it really work?


  • What is image recognition? 
  • Importance of image recognition
  • Image recognition vs Computer vision
  • How does image recognition actually work?

In this blog, we will understand what image recognition is and how it really works. Image recognition is the process of identifying an object or a feature in an image or a video. It is also used in various applications such as defect detection, medical imaging, security surveillance, etc. 

Basically, image recognition is a type of AI programming. It assigns a single, high-level label to an image by analyzing and interpreting its pixel patterns. Although it is a sub-category of computer vision, image recognition is also related to image processing. For the unversed, image processing is a catch-all term for using ML algorithms for analyzing digital images. 

Let us understand what exactly image recognition is and why it is important. 

What is image recognition?

In the case of humans, our brains make it easier to identify and name things. Whether it is a dog or a cat or a flying saucer, our brain makes vision easy. However, in the case of a computer, it is quite hard to imitate and identify things. 

Certainly, image recognition is a sub-category of Computer Vision as well as Artificial Intelligence. It represents a set of methods to detect and analyze images in order to enable the automation of a particular task. It further allows computers and machines to identify places, people, objects, and other elements present in an image. As a result, it helps draw conclusions from them through analysis. 

Whether it is an image or a video, you can carry out image recognition at different degrees of accuracy, based on the type of information or required concept. In fact, a model or an algorithm can detect a particular element just as it can simply assign an image to a large category. 

In simple words, image recognition is basically:

 “a digital image or video process to identify and detect an object or feature, and AI is increasingly being highly effective in using this technology.”

What are the different tasks image recognition can perform?

Image recognition can perform several tasks. These are, namely—Classification, Tagging, Detection as well as Segmentation. 

  • Classification is the identification of the class or category to which an image belongs.
  • Tagging is also a classification task. However, it comes with a higher degree of accuracy. Image recognition can recognize the presence of various concepts or objects within an image. Therefore, one or more tags can be assigned to a particular image. 
  • Detection is necessary for locating an object in an image. After the object is located, a bounding box is placed around the object in question. 
  • Segmentation is also a detection task but it can locate an element on an image to the nearest pixel. In some cases, it is essentially necessary to be extremely precise for the development of autonomous cars.

What is the importance of image recognition?

To be precise, image recognition is an important and widely-used computer vision task or application. Recognizing image patterns and extracting features is a building block of other more complex computer vision techniques. However, it also has various specific applications which make it a necessary machine learning task.

Specifically, this broad and highly-generalizable functionality of image recognition allows several transformative user experiences. It includes Automated image organization, User-generated content moderation, Enhanced visual search, Automated photo and video tagging, and Interactive marketing/Creative campaigns.

Image recognition has the potential to accelerate. It can process images faster—or more accurately than manual image inspection. It is, therefore, a vital technique used in many applications. Image recognition is the main facilitator in deep learning applications. For example,

  • Visual inspection: In manufacturing, identifying parts as defective or non-defective can quickly inspect thousands of parts on an assembly line.
  • Image Classification: It is the process of categorizing images based on their content. It is especially useful in e-commerce applications like image retrieval and recommender systems.
  • Autonomous Driving: Recognizing a stop sign or a pedestrian in an image is critical for autonomous driving applications.
  • Robotics: Robots can use image recognition to identify objects and improve autonomous navigation. They are certainly able to do so by identifying locations or objects on their path.

Basically, image recognition is the core technology facilitating various applications. Therefore, it helps identify objects or scenes. It further uses that information for making decisions as a part of a larger system. Moreover, image recognition helps these systems become aware and thus, enables better decisions by providing insight to the system. 

What are the modes and types of image recognition?

Image recognition is a broad and diverse computer vision task related to the broader problem of pattern recognition. As a result, there are a few key distinctions to make when determining which solution is best for the problem at hand.

Mainly, image recognition can be divided into 2 categories:

  • Single class recognition
  • Multiclass recognition

Let us understand through an example. Generally, the picture of a dog and cat will be assigned a single label, if you are training a dog or cat recognition model. These models are called binary classifiers only when two classes are involved—dog or no dog. 

On the other hand, multiclass recognition models can assign various labels to an image. An image with a dog and a car can have one label each. Typically, multiclass models output a confidence score for each possible class. Most importantly, they describe the probability that the image belongs to that class.  

However, there are numerous traditional statistical approaches to image recognition. Certainly, these are linear classifiers, Bayesian classification, support vector machines, decision trees, and many more.  

Image Recognition vs Computer Vision

Although the terms are often interchangeably used, customer vision and image recognition are different concepts. Let us dive deep into the differences between the two terms: 

Image RecognitionComputer Vision
It is mainly focused on processing the raw input images in order to enhance them or preparing them to do other tasks.   Computer Vision is focused on extracting information from the input images or videos for a proper understanding of them in order to predict the visual input like a human brain.     
It uses methods like Anisotropic diffusion, Hidden Markov models, independent component analysis, Different Filtering, etc. Image processing is used for computer vision along with other ML techniques, CNN, and many more.     
On one hand, image Processing is a subset of Computer Vision. On the other hand, computer Vision is a superset of Image Processing.  
For e.g. Rescaling image (Digital Zoom), Correcting illumination, Changing tones, etc. For e.g. Object detection, Face detection, Handwriting recognition, etc.  

How does image recognition actually work?

Image recognition algorithms are responsible for facilitating the image recognition process. Let us see how an AI image recognition algorithm is built. 

  1. The AI image recognition algorithm process begins with accumulating and organizing the raw data. In this process, computers interpret every image as a raster or a vector image. Hence, they are unable to differentiate various sets of images. 

While raster images are bitmaps where individual pixels collectively forming an image are arranged as a grid, vector images are a set of polygons with explanations for various colors.

  • The next step is organizing data. By organizing data, we certainly mean categorizing each image and extracting the physical features. In this process, a geometric encoding of the images is converted into labels physically describing the images—which are further analyzed by software. Therefore, it is critical to properly gather and organize the data for training the model. This is because hampering the quality of data at this stage makes it incapable of recognizing patterns during the further stages. 
  • Additionally, creating a predictive model is the next step and the final step is utilizing the model for comprehending the images. Therefore, it is vital to ensure that algorithms are written with proper care. This is because a single anomaly can make the entire model futile. Therefore, these algorithms are explicitly written by people with deep expertise in applied mathematics. 

Above all, image recognition algorithms use deep learning datasets for identifying patterns in the images. So, these datasets constitute numerous labeled images. Lastly, the algorithm goes through these datasets and learns what an image of a specific object looks like.  

Author Bio: –Samruddhi Chaporkar is a Technical Content Writer and Digital Marketer. She is passionate about sharing Tech solutions with the hope to make a difference in people’s lives and contribute to their professional growth. you can follow her on LinkedIn and Twitter.

Nancy Yates

Nancy Yates is a trend researcher by passion, a digital marketing expert, and a professional business and tech blogger. As a tech knowledge, Nancy Yates eagerly looks for the ins and outs of modern tech growths.