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Image Annotation

What is Image Annotation and What are its Applications?

To be specific, image annotation can be defined as the procedure of adding and attaching labels to an image to provide transcription of the image and convert it into an informational visual. You can either add a single label to any image or can add multiple labels to various parts of the images as per your requirement to make your image data more informational. The labels or metadata that are attached to the images are used to identify characteristics of the data which you want the machines to analyze and understand. 

In order to create high-quality and accurate datasets for training purposes, image annotation is very important as the required datasets can not be produced without using the correct procedure. After the images are attenuated they are then used to train your AI-based projects, computer vision, or machine learning models.

To make this much simpler, let us understand image annotation with the help of an example. Suppose you want a machine or a project to distinguish an animal, say a dog from others, then you must first make the machine learn what a dog is, and how it looks to make it easily differentiate from other animals. To do that you have to attenuate or label the different parts of the dog’s image like ear, nose, tail, legs, etc, and create a data set that will help the machine to understand and learn how the dog looks. This process of labelling the different parts of the image is termed image attenuation. When you use the attenuated dog’s image to let the machine learn about dogs, it can later differentiate the dog from other animals even in the images that are not attenuated or labelled. This way machines can be trained with the help of the datasets to learn about the animals.

Nowadays, many organizations have started using image attenuation technology to develop AI projects. The reason behind this is that images can be labelled or attenuated easily and the datasets can be used to train the machines quickly and efficiently.

How Can You Annotate Images and Videos?

There are two ways to annotate the required images and videos. One is you can do it by yourself, for which you would need an open-source or paid data annotation tool. While in the second option, you can outsource the image and video annotation services to the professionals in the market and they will be annotating the images and videos for you as per your requirement.

It is always wise to take the help of third-party image annotation service providers if you have a large amount of data and want to annotate a good amount of images. Third-party vendors have a professional and experienced workforce that is specifically trained to annotate the images in order to provide you with high-quality results. But if you think that you do not want to outsource your work to the vendors then you can even choose to develop your own image annotation tool which can be a bit time-consuming and capital-intensive approach. 

If you are planning to annotate the images and videos by your own self then you can follow the below steps that will help you annotate the images and videos quickly and efficiently.

  • In the first step, you need to prepare and get ready with your image dataset.
  • Now, you need to specify the class labels of objects that you want your machines or projects to detect.
  • In the third step, you need to perform various image annotation techniques including bounding box annotation, 3D, cuboid annotation, polygon annotation, etc to detect the desired objects on the images.
  • In the fourth step, you have to choose the class label for every box that you have drawn in the previous step.
  • Finally, you can now export the image annotations in the required format.

Why Image Annotation is Essential?

Image attenuation or adding labels to your images is a crucial process that needs to be followed if you are working on an AI project and want your machines to learn the important part and objects of the images. Such a process helps machines to identify the objects in the never-before-seen images.

Following are a few situations, where image annotation is extremely crucial:

1. For New Tasks

Image annotation is a must if AI technology is used for new AI tasks for which no appropriate annotated data is available. An example of such a situation is the use of computer vision in industrial automation of a car manufacturer, if there is no data set of spare parts, the AI machine will not be able to recognize the aforementioned parts to use in the assembly line. 

2. For Restricted Data

Suppose you have plenty of annotated data available on the internet, but the images that you are using need a licence agreement which is most probably restricted, then in such a situation you need to annotate your images manually to fulfil the requirement.

Applications of Image Annotation

Overseeing the benefits of image annotation in AI projects, many organizations use the process to attain high-quality image data or datasets and train machines as per the project’s requirements. 

Following are a few of the many applications that Image Annotation has in various industries:

1. In Agriculture Industry

Image annotation has explored a wide application in the agriculture industry. Using the process, a lot of landowners and people related to the agriculture industry have benefitted from it. 

In the Agriculture field, farmers use satellite images along with drones to get the actual position of the crops and the soil to take appropriate actions. Image annotation has a wide role in the process as it is used to annotate the images that are captured by the camera to make the machines learn the difference between the weeds and the crops. 

The data created is later used by the landowners or farmers to figure out the area where the weed is growing and leave the area where the weed is not growing. Such data helps in using pesticides to the required area and not to the entire field. This ultimately helps landowners and the farmers save money as pesticides are used only on the required areas and not on the entire field. 

2. In Manufacturing Industry

Providing advantages to various sectors and industries, image annotation has benefitted the manufacturing sector too. With the advancement in time and technology, manufacturers have understood the capability of image annotation in AI. They know that it is one of the best technologies that they can rely on to capture the accurate information of their warehouse inventory. 

Manufacturing business owners are now using the technology to get the image datasets which helps the machine in determining which product will get out of stock soon and need to be restocked. This ultimately helps in determining which product is sufficient for the entire month and does not need restocking. Using image annotation, manufacturers can save on both money and time as they do not need to manually check and manage their inventory. They can now invest the money in the products only when they are about to fall short.

3. In Healthcare Industry

Both AI and image annotation have found their way into the healthcare industry and that too with great efficiency. The fact of the matter is that even doctors are using the technology and its related solutions for a quick diagnosis of the patients they are treating.

There are many benefits that image annotation has in the healthcare domain and a brief example of it is the use of technology in radiology. AI along with Image annotation is capable to analyze radiology images and understand them to diagnose cancer symptoms if present. 

To achieve this, the professionals need to train the AI-powered solution using various annotated images that are labeled with cancerous and non-cancerous spots. By doing this the machine can understand the difference between a cancerous and a non-cancerous image and can give accurate results in the real-time diagnosis of the patients.

4. In Retail Industry

Last but not least, image annotation has shown many use cases and applications in the retail industry when it comes to developing high-quality and efficient AI-powered solutions for retail outlets, be it offline or online. Image annotation has helped the retail outlets owners to not only increase the footfall of the customers at the store but also improve their shopping experience. 

Image annotation helps in building AI-powered solutions that are capable of understanding the customer’s requirements and then presenting the relevant results after looking into the product catalogs and fetching the correct lists of products available. Image attenuation helps the machines understand the searches that the customers are doing on the store based on which it checks for availability and displays the results to the customer. For eg: If a customer searches for jeans in the store, then the machine or the software will analyze the customer’s requirements and will present all the jeans options available at the store in front of the customer. This will be really quick and will ultimately boost the shopping experience of the customers.

With advancements in AI technology, nowadays, real store owners have also started using AI-powered robots in their physical stores and outlets. These robots are trained to click the product shelves images and then analyze and determine the products that are low in quantity and can go out of stock soon. This helps in automating the entire inventory management process for the retail outlets thereby saving both time and money.

5. In Finance Industry

It is true that the finance industry is still behind to reap the benefits of image annotation to a great extent, still, there are many players in the industry that are already leveraging the power of image annotation in their ongoing AI-powered projects. The finance sector has been using image annotation in improving the security of the ATM transactions that the customers are undergoing. 

Many banks are nowadays using facial recognition technology to analyze and verify the customer’s identity that is accessing the ATM and is withdrawing money from it. The entire procedure that involves analyzing and verifying the customer information is done using the image annotation process known as pose-point. 

As banks develop, they’re using AI to improve their ATM transaction’s security, they use pose-point to map the facial impression and features of the customers and that includes eyes, nose, lips, etc. Using the technology, the machine can easily identify that the correct individual is making the transactions from the dedicated ATM card, This ultimately reduces the chances of fraudulent activities and unwanted threats. Besides this, Image annotation is even used for annotating receipts for reimbursement or checks to deposit via a mobile device.

To Conclude

To achieve high-quality and functional datasets, image annotation is crucial. Professionals use this data set to help the machine analyze and understand the images in-depth and produce accurate results in real-time situations. 

Image annotation helps the machines to quickly learn about differentiating objects from each other and this helps the machine in learning and advancing to such an extent that with enough time and training they can analyze and determine the images that have never been seen before.

In this blog post, we have not only talked about what is image annotation and how it actually works but have also kept focussed on the technology that is supporting AI and is benefitting different sectors in the market. 

Nowadays, many companies are developing AI projects that are highly dependent on AI image annotation and the reason is that the project involves training machines to differentiate various objects which require high-quality datasets. Image annotation helps to achieve these data sets easily. Almost every sector in the market has started using AI and image annotation in developing solutions that can increase their work efficiency to produce better output and high-quality results. 

Also, undergoing the image attenuation process to achieve transcription of image has become extremely important for various businesses and companies that are building to create a comprehensive, efficient image annotation process.

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