Image Recognition: Categories and Uses

“Art is the imposing of a pattern on experience, and our aesthetic enjoyment is recognition of the pattern.”

― Alfred North Whitehead

 

The growth of artificial intelligence has created new development opportunities for every organization and sector. Businesses have begun to employ computer vision—and specifically image recognition—to streamline their operations and boost productivity.

 

In this blog you will learn more about Image Recognition.

 

What is Image Recognition?

 

With the help of technology, we can recognise many elements in photos, including objects, people, entities, and other things. Users are able to share a vast amount of data through social media, apps, and internet usage in the modern era. A lot of digital photos and videos can also be produced by the increase in smartphones and androids with high-resolution cameras. In order to provide better services, the industries make widespread use of digital data.

 

The technique of identifying the object or attribute in digital photos or videos is known as image recognition, and it is a part of computer vision technology.

 

Categories of Image Recognition Tasks

 

Image recognition can be achieved with varying degrees of precision depending upon the type of data needed. Just as it can classify an image into a broad category easily, a model or algorithm can help to identify the precise element. Categories of image recognition tasks are given below :

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Categories of Image Recognition Tasks

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1. Classification

 

This term refers to the identification of the category or class, to which the image belongs. An image can have only one class.

 

2. Tagging 

 

This task of classification is more precise than other types. Multiple objects in a picture can be identified with this category. A specific image can also have multiple tags applied to it.

 

3. Localization

 

It assists in inserting the image in the appropriate class and draws a boundary box around the object to indicate where it is located in the image.

 

4. Detection 

 

In order to identify the various objects in the image and generate a boundary box around it, it is helpful to classify the objects. Detection is a classification variation with localization tasks for various objects.

 

5. Semantic Segmentation

 

Semantic Segmentation enables precision of pixel-level location of an element in an image. In some circumstances, like in the creation of autonomous vehicles, it is important that the outcomes are exceedingly exact.

 

6. Instance segmentation

 

It helps to distinguish between various items of the same class.

 

Also Read | How does Facial Recognition Work? 

 

How does Image Recognition Work?

 

A number matrix can be represented as a digital image. This figure provides the necessary information pertaining to the pixels of the image. The different intensities of the pixels combine together to generate an average of a single value, which is thereafter displayed in matrix form.

 

The intensity and location of different pixels within the image are the inputs to the recognition algorithm. Using this data, one may teach the system how to map out the relationships and patterns in various photos.

 

After the training is complete, one can examine the system's performance using test data. To improve the systems' performance and obtain the exact results for picture recognition, neural network weights can be periodically adjusted. 

 

Therefore, using the deep learning algorithms, neural networks can process and compare the numerical results with some specific parameters to achieve the desired output.

 

The most common algorithms used in the process of image recognition are Speeded Up Robust Features(SURF), Scale-invariant Feature Transform(SIFT), and PCA(Principal Component Analysis).

 

The various components in the working of image recognition are given below.

 

1. Neural Network Structure

 

There are various kinds of neural networks, and they are all very helpful for recognising images. Convolution neural networks (CNN) uses a special working concept to provide the best results with the help of deep learning image recognition. There are many other variations of CNN architecture.

 

2. Input Layer

 

An input layer and the servers serve as the neural network's entrance in the vast majority of CNN architectures. According to the type of input provided, it incorporates the numerical data into a machine learning algorithm. 

 

It may be represented in various ways; for example, an RGB image may represent a cube matrix whereas a monochrome image may represent a square array.

 

3. Hidden Layer

 

The convolution layer, batch normalization layer, activation function layer, and pooling layer are parts of the hidden CNN layers. These layers are given below.

 

• Convolution Layer

 

A connected layer presented in a classical design, where each value can serve as an input to each layer's neuron, is completely different from how CNN architecture normally operates. 

 

Instead of this, CNN creates feature maps by using filters or kernels. It can be a 2D or a 3D matrix, depending upon the input image, and its members that are trainable weights.

 

• Batch normalization

 

It is a particular mathematical function with the parameters of expectation and variance. Its function is to standardize the values and help to  make them equal in a specific range that can be suitable for activation function. The normalizing process occurs before the activation function.

 

The reduction of training time and the improvement of system performance are the primary goals of normalization. Each layer can be configured separately with minimum interdependence thanks to this feature.

 

• Activation Function 

 

A barrier that does not pass any specific values is the activation function. Due to this reason, a lot of mathematical operations use neural network techniques and computer vision. Rectified Linear Unit Activation function (ReLU) is an alternative for picture recognition . It works by checking each array element, and if the value is negative, zero(0) should be substituted.

 

• Pooling Layer

 

In order to reduce the size of the input layer, the pooling layer allocates the average value inside the kernel-defined region. The pooling layer is a very important phase. If it is absent, then both the input and output will point in the same direction, which will increase the number of variables that can be changed, which necessitates a lot more computational effort, and lowers the algorithm's effectiveness.

 

4. Output Layer

 

A few neurons form the output layer, and each neuron represents a certain class of algorithms. Using a softmax function, output values can be adjusted so that their sum is equal to 1. The most important value can be used by the network to determine which class the input image belongs to.

 

Also Read | Object Recognition vs Image Recognition

 

 

Challenges of Image Recognition

 

The challenges faced by image recognition are given below.

 

1. Viewpoint Variation

 

The objects in the image are generally not aligned in the same way as they are in reality. Such images may cause the image recognition system to forecast incorrect values when they are being used as input. The biggest picture identification problem may arise as a result of the system's inability to understand the image's alignment changes.

 

2. Scale Variation 

 

Scale variation has a huge impact on how the image's items are being classified. Moving in closer might make the image appear larger, and vice versa. It alters the dimension of the image and produces false results.

 

3. Deformation 

 

Even when deformed, objects remain unchanged. The system can learn from the image and determine that an object might only have a certain shape. As a result of image and shape changes in the real world, there might be an inaccuracy in the outcome that is displayed by the system.

 

4. Inter-class Variation

 

Within the same class, specific objects vary. They may vary in size and shape but may still serve to represent the same class. For instance, there are various variations of bottles, chairs, and buttons.

 

5. Occlusion

 

Occasionally, the object may obstruct the entire image, which finally results in the system receiving partial data. A sensitive algorithm with a wide variety of sample data must be developed to account for these variances.

 

To train the neural network models effectively, the training should have varieties in both single-class and multiple-class connections. The options will ensure that when tested on test data, the model predicts reliable outcomes. It takes lots of time to determine whether the sample data needed to derive conclusions is adequate since the samples are present in random order.

 

Also Read | What is Biometrics and How Does it Work?

 

Uses of Image Recognition in Different Industries

 

The technology of deep learning image recognition is largely employed and has a big impact on many industries as well as in our daily life. Some common uses of Image Recognition are given below.

 

1. Healthcare

 

Despite years of training and experience, doctors might still occasionally make occasional errors, just like any other person, when dealing with a large number of patients. In order to provide experts in medical specialties with AI support, many healthcare facilities have developed an image recognition system.

 

Famous examples of deep learning algorithms that are being used to analyze radiology results of patients include CT, MRI, and X-rays. The neural network model helps medical professionals to identify deviations and give precise diagnoses to improve the processing of results overall.

 

2. Manufacturing

 

Evaluation of the daily critical points on the premises is part of the evaluation of the production lines. To assess the level of the finished goods and reduce flaws, image recognition is widely utilized. Manufacturing industries can control numerous systemic processes with the aid of worker condition assessments.

 

3. Autonomous Vehicles

 

Autonomous vehicles can analyze traffic patterns and take appropriate action with the help of image recognition. The logistical industry can also use small robots with image recognition to detect and move things from one location to another. It can be used to keep a record of the product's movement history and guard it against theft.

 

Numerous driver-assistance features can be found in modern vehicles, which allows people to avoid collisions and maintain control while driving securely. 

 

The vehicle can recognise the surroundings in real time as well as the traffic signs and other items on the road with the help of ML algorithms. Self-driving cars have been predicted to be the more advanced version of this technology in the future.

 

4. Military Surveillance 

 

Image recognition helps in spotting suspicious activity near border areas and helps in taking automated judgments that can stop infiltration and save soldiers' lives.

 

5. eCommerce

 

One of the industries that is currently growing quickly is eCommerce. A visual search powered by deep learning algorithms is one of the fastest growing eCommerce trends. Customers today can snap images and find out where they can buy them, using Google Lens as an example.

 

In social media, where businesses can correctly identify the target demographic and efficiently understand their habits, personality, and preferences, ecommerce adopts image recognition technology to recognise the brands and trademarks on the images.

 

6. Education

 

With the help of deep learning techniques, various aspects of the education sector have been improved. Currently, cameras are being used in online learning environments, making it difficult for professors to monitor students' facial expressions. 

 

The neural networks model can be used to examine how engaged students are in the learning process as well as their body language and facial expressions.

 

Additionally, image recognition helps to provide automated exam proctoring, digitalization of instructional materials, handwriting recognition, attendance tracking, and campus security.

 

7. Social Media

 

Every day, hundreds of photographs and videos are being processed by social media networks. In addition to automating content moderation in order to prevent the publication of social network illegal content, picture recognition helps in a considerable classification of a photo collection through image cataloging.

 

Additionally, by observing social media text postings that include their brands, one can discover how their consumers feel about, connect with, and talk about their businesses.

 

8. Visual Impairment Aid

 

Visual impairment, commonly referred to as vision impairment, is a decline in vision that may result in issues that cannot be resolved by conventional methods. Social networking was primarily text-based before, but the technology has started to adjust to vision impairment.

 

In order to provide visually impaired people with unique experiences like their counterparts on social media, image recognition helps in social media design and navigation. 

 

One such programme that helps to find and recognise things is called Aipoly. When the user points their phone's camera at the object they want to analyze, the application will describe what it sees. In order to recognise the precise object, the application uses deep learning algorithms.

 

Also Read | Soft Biometrics

 

Because of their superior high-level image interpretation, parallel processing, and contextual awareness humans do visual tasks significantly better than computers do. However, after a period of time, human abilities drastically deteriorate. 

 

Some working environments are also inaccessible to or too dangerous for humans. Because of this, various applications for automatic recognition systems have been developed.