One of the most important stages in the development of computer vision and image recognition applications is image annotation, which involves recognizing, obtaining, describing, and interpreting results from digital images or videos. Computer vision is widely used in artificial intelligence (AI) applications such as autonomous vehicles, medical imaging, and security. As a result, image annotation is critical in many fields of AI/ML development. Continue reading to learn more about image annotation.
What exactly is image annotation?
To function properly, supervised ML models require data labeling. Image annotation is a type of data labeling that focuses solely on visual digital data such as images and videos.
Image annotation frequently necessitates manual labor. An engineer creates the labels or “tags” and sends the image-specific data to the computer vision model that is being trained. Consider this process to be similar to the questions a child asks her parents to learn about her surroundings. The data classified by the parents into universal phrases such as bananas, oranges, cats, and so on.
Annotating images typically involves one of five main methods:
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Bounding Box,
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Landmarking
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Masking
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Polygon
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Polyline
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Bounding Boxes
A selection box made around the target for inspection. Both 2D and 3D images can benefit from the use of bounding boxes.
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Landmarking
Landmarking is a reliable method for recognizing people’s actions, expressions, and emotions based on the unique characteristics of their faces and bodies. In addition, it serves as a visual indicator of the user’s physical orientation and position. The parts of the human face learned by an ML model using the data labelers assign to various areas of the face (eyes, eyebrows, lips, forehead, etc.) as shown in the figure below.
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Masking
Annotations at the pixel level used to obscure parts of an image while drawing attention to others. This method is similar to applying a filter to an image so that you can zero in on specific details.
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Polygon
This method employed to designate the object’s pick point and outline its perimeters: Labeling objects of an irregular shape can benefit from the polygon method.
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Polyline
Using the polyline method, ML models for computer vision can developed, which can then used to direct autonomous vehicles. It makes sure that ML models can identify roadside features, routes, and turns, as well as detect oncoming vehicles, all of which are crucial for safe driving.
Mark up photos and videos with annotations
An image annotation tool is essential for your business so that visual data can be properly tagged. These kinds of resources can purchased from third-party vendors. You can also find free image labeling tools that use open-source software. In addition, you can tailor them to fit the specific requirements of your company because they are adaptable.
It is possible to avoid spending money on third-party software by creating your image annotation tool. It is more expensive and time-consuming to do everything in-house. But if you have the time and money to do so and find that none of the commercially available tools is quite right for you, you can always create your own.
Exactly why has image annotation become so important recently?
Whether it’s in healthcare, automobiles, or advertising, computer vision is already making a difference in our daily lives. PWC estimates that self-driving cars could account for 40% of all miles driven by 2030, and Forbes estimates the computer vision market value will be around $50 billion by 2022.
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Self-driving cars that use image annotation
Creating diverse and accurate annotations on datasets for autonomous vehicle training, validation, and testing made possible through the use of image annotation, which is a boon to the automotive industry in many ways. Aside from the obvious applications of motion analysis in driver assistance systems, self-driving cars, and automated car rental systems, Cars can now detect if a driver is active, sleepy, fatigued, etc., and take appropriate action to improve road safety. The evolution of autonomous vehicles is being accelerated by artificial intelligence technology used for annotating images. Since AI enables self-driving cars to adapt instantly to changing conditions and learn from new situations, they are inherently safer than human drivers.
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Annotation of images for use in scientific research
Annotating medical images is another way to collect data for use in healthcare training datasets for AI and machine learning. Medical images such as X-rays, CT scans, MRI scans, and Ultrasounds annotated and used to train models for automatically diagnosing various diseases. Expert radiologist doctors perform the task of medical image annotation with precision, manually annotating each image with the appropriate tool to ensure that the disease is distinguishable to AI machines that detect similar indications in clinical practice. Annotating medical images helps doctors spot and understand subtle shifts.
The Process of Annotating Images Rising consumer interest in these key sectors
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E-Commerce
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Manufacturing
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Healthcare
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Automotive
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Retail
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Finance
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Agriculture
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Transportation & Logistics
The process of annotating images is partially manual. Organizations face a crucial strategic decision when deciding who should carry out this manual task. This is because the three primary approaches all have their advantages and disadvantages in terms of cost, output quality, data security, etc.
In the context of algorithms that facilitate rapid searching and retrieval of images from large datasets, the contribution of image annotation is growing in significance. Once the images have been properly annotated, they can used to train a machine learning algorithm and yield reliable results.
