Vision is one of humanity’s greatest assets. It is through sight that humans can discern different objects and the world around them, leading to the evolution of civilization itself. Over time, humanity also learned to capture its visions and store them in the form of images and videos. And now, technology has advanced to the level where computers are learning to view the world around them through images and videos.
The technology that makes it possible is Computer Vision, one of the most critical fields in Artificial intelligence. At its core, the ability of this technology to enable machines to draw meaningful information from digital content is driven by image annotation. The force enabling businesses to utilize this wonderful opportunity is image annotation services experts, i.e., professionals who label visual data for AI/ML model development.
With demand for AI increasing from all industry niches due to the many advantages it offers, the pressure is always on annotation experts to deliver accurate results quickly. To reach the projected global AI economic contribution of US$15.7 Trillion by 2030, they have adopted a slew of complex techniques and sophisticated artificial intelligence tools.
This quick-help guide details how they use those tools to achieve the near-perfect levels of the accuracy demanded from them.
What Is Image Annotation?
As mentioned earlier, computers now can interpret visual data gained from cameras present in an environment. This ability, however, is not inherent, unlike humans. It needs to be induced by training the AI/ML algorithms to recognize what they are “seeing”.
This is where image annotation comes into the picture. Experts at an image annotation company take the training images and demarcate the target subject/object in them using various techniques to help the algorithms differentiate between that and the unwanted elements in the image.
Thus, image annotation can be said to be the process by which annotators tag pertinent elements of an image/video frame to aid an ML algorithm to recognize the target object.
To develop an AI that can accurately distinguish the target subject in real-world image samples, possibly in real-time, hundreds of thousands to millions of such annotated images are used. This need for a high number of samples makes annotation a grueling and tedious job. It becomes impractical for data samples in such high numbers too. It is for such circumstances that the technique of Deep Learning has been developed.
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In deep learning, image annotation services experts train Machine Learning models to learn to annotate the images themselves. Multiple layers of training algorithms are created with a hierarchy, and each level improves the recognition accuracy a bit more. The final result is an accurate AI model that can distinguish what needs to be in images because it has gone through a large data set of the same during training.
Image labeling is another term often used in place of data annotation. These two terms are used interchangeably as there are only subtle differences between the two. While annotation deals with tagging important aspects of images, labeling deals with contextualizing them. Labeling is also what the algorithm does when it distinguishes target elements by itself. Therefore, data labeling services agencies can also be hired to perform image annotation.
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Types Of Image Annotation Services In Practice
AI is finding use in a variety of applications, meaning an AI model is to be trained according to the specifics of its respective application. To match the demand, there are various annotation techniques for images used by experts. By taking this application-specific approach, annotators save end-users development time and costs, thereby speeding up the adoption of AI everywhere.
Presently, there are four main types of image annotation in practice:
Classification
Classification is the most basic form of annotation, focussing solely on the image under consideration as a whole. A simple tag is used to differentiate one image from another. Its primary function is to help the AI capture abstract information and detect similar images in the data set.
Experts at the image annotation company assign the images in question to various classes. These classes are created based on image attributes like features, time of capture, etc. The AI developed using this can identify if an object is present in a particular image and determine its class. The technique also helps the AI identify an unlabeled image of a class similar to the one in use during training.
Object Detection and Recognition
Object recognition and detection build on Classification by including more information on top of the basic class-based labeling. The target object’s location and quantity form the additional information considered. The advantage offered by this type of annotation is that multiple classes can be identified for a single image in contrast to Classification where the entire image gets put under a single class.
Data labeling services experts introduce boundaries around the target objects via different techniques to distinguish them from the rest of the image. These aid in locating and tracking the target object when it’s in motion, like with motion detection. The bound objects can carry unique identifiers to distinguish one from the other. The identifiers could be name, size, shape, color, etc.
Segmentation
In this type of annotation, experts divide the target object into multiple segments and then label them for easy identification. It is used to add precision to the annotation process by taking object class assignment down to the pixel level every pixel gets assigned a class. The high level of accuracy also means that it is the most difficult type of annotation on the list.
Segmentation comes with three sub-categories:
- Semantic Segmentation
Image annotation company experts use semantic segmentation to distinguish one object from another when they are similar. The results that this sub-type of annotation produces are highly accurate for object attributes like existence, size, shape, etc. Thus, individual objects of similar type can be easily demarcated in an image.
- Instance Segmentation
Instance segmentation helps identify the presence of an object in an image along with its location. Experts perform this sub-type of annotation when the presence of an object is to be ascertained in an image. The object can then be labeled, thereby helping to remove the unwanted elements in the image.
- Panoptic Segmentation
It is a combination of semantic and instance segmentation sub-types and is used to provide the system the capability to label an image’s background and the target objects in it. It is done by image annotation services agency experts when an all-in-one approach is needed for AI development.
- Boundary Identification
It is a type of image annotation that doesn’t get the spotlight like the other three mentioned above. It is often used in conjunction with others but can also be used independently whenever required. It is applicable in situations when an AI needs to be trained to identify lines and curves in an image. An example is a self-driving AI recognizing lane marking lines on roads to guide the vehicle accurately.
Image Annotation Techniques Used By Data Labeling Services
The types of image annotation mentioned earlier are made possible by the use of various sophisticated techniques. They involve drawing demarcation entities like lines and boxes to aid in distinguishing one object from another.
Bounding Box
As the name suggests, experts at the image annotation company performing this operation draw boxes around the target objects in an image. This technique is most helpful when the objects are symmetrical, their shape is not an issue, and neither is its occlusion. The box used can either be two or three-dimensional.
Landmarking
It is used to point out certain characteristics in the image data by plotting them. It can also be used to locate a particular point in the image independently or relate its position to other points using pose-point annotations. It is useful in cases where tracking the various reference points is required, like recognizing facial expressions based on facial movements like smiling.
Masking
This annotation technique is used when there is a need to focus on certain sections of the image. The unwanted sections are hidden by using a virtual mask at the pixel level.
Polygon
Perhaps the most recognized technique, polygon drawing by image annotation services have experts marking the peaks of the target object all around its edges. This technique is most helpful when there is a need to demarcate an irregularly-shaped object. It also yields a high level of precision as a result.
Transcription
Images sometimes contain textual content in them that needs to be treated as textual data only. In such instances, the transcription technique is used by annotators. The text or video segment gets annotated and separated for consideration.
Polyline
One or more line segments are laid successively to make continuous lines to be plotted. Open shapes like roads, sidewalks, cloth dryer strings, etc. are the main target objects here.
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Conclusion
Image and video data occupy an increasing share of the total data generated annually. This surge in these media types presents a lucrative opportunity for businesses if they can get the maximum out of the images. By hiring a data annotation company to annotate those images, your company gets the experience and expertise of the company’s annotators and the latest technologies/techniques in use. You can develop the AI you want that can drive efficiency and conversions across your markets and within your company simultaneously.
