How Image Annotation is done?
Let’s suppose a situation. You want to make an AI model that detects the emotions or moods of a person. The system would see a bunch of people's faces, and based on their features, AI would tell if a person is happy, sad, angry or more. Now the question that arises is, how would we train the model to recognize different emotions?
The journey of any AI model starts with data collection, and data annotation. In this case, the image dataset would be collected and then it would be annotated by a bunch of human annotators, software's, or both. But to understand all of this, we need to first understand what image data annotation is.
What is Image Annotation?
Have you used the feature of Google lens where you point your camera to any image or object, then it would tell more information about it? Like, suppose you wanted to buy a specific type of shoe. You clicked a picture of that shoe on Google lens and it would tell you all the matching shoes from all over the internet. You can then go ahead and buy it. This is such a cool innovation, but how is it done? With the help of feeding high-quality and accurate Data Annotation Services to the AI training model. Now we can understand what image annotation is?
Image annotation is nothing but the process of labelling images by adding shapes, drawings, labels or comments to train the machine learning model. Suppose we want the ML model to recognize different animals, and also classify them like a cat, dog, cow, and more. To do this, we need to tell the AI what a cat looks like, what a dog looks like, and more.
What are the types of Image Annotation?
Depending on the project you are working on, there are different scenarios when it comes to image annotation. Some projects demand object detection of only one class, and in some, AI may want to know everything in the image (like in self-driving vehicles).
Mainly, there are 4 kinds of image annotation, these are:
1. Image Classification: This is the most basic type, in which items are broadly categorized. Identifying components such as vehicles, buildings, and traffic signals is all that is required here.
2. Object Detection: A slightly more specific function that identifies and annotates various objects. For example cars and taxis, buildings and skyscrapers, lanes 1, 2, 3, and more.
3. Image Segmentation: This goes into the details of each image. It includes providing information about an object, such as color, location, and appearance, to assist machines in distinguishing it. For example, a black car in lane 2 would be the car in the center.
4. Object Tracking: This involves detecting an object's features over multiple frames in the same dataset, such as location and other attributes. Object movements and patterns can be studied using video and surveillance camera footage.
What are the image annotation techniques?
There are different types of techniques that are used in image annotation, some of them are:
Bounding Boxes: This is the most basic image annotation technique in which annotators draw a box around an object to attribute object-specific details. This technique is best for annotating things that are symmetrical in shape.
3D Bounding Boxes: The 3D bounding boxes are three-dimensional versions of bounding boxes, which are in 2D. The cuboids track objects in three-dimension for gathering precise information.3D boxes are useful when the AI model is determining height, width and depth.
Landmarks: This technique highlights the details of object movement in a video or an image. Landmarking is most common in annotating facial characteristics, motions, expressions, postures, and more in facial recognition.
Polygon: Objects in images don't need to be symmetrical or regular. And there are various cases in which you can find images like this, and here the polygon annotation is used.
Annotators use the polygon technique to precisely annotate irregular shapes and objects in these circumstances. This method entails manually drawing lines around an object's circumference or perimeter and adding dots across its dimensions.
Line Annotation: Apart from drawing boxes and polygons, lines are also used in annotating objects in images. This method is especially used by self-driving cars to detect different lanes and boundaries.
How Image Annotation is done?
Image annotation can be done by:
Step 1: Preparing the image dataset
Step 2: Specifying the different classes and labels to detect in an image
Step 3: Drawing the type of annotation (Boxes (2D, 3D), polygon, lines)
Step 4: Selecting the class label for each Image
Step 5: Exporting the image in the required format.
What are the use cases of Image Annotation?
There is a wide variety of applications for AI, but here we are going to look at some of the use cases of image annotation.
1. Retail Industry: In a shopping mall or a grocery store, the 2D bounding box technique can be used to identify photos of in-store objects, such as shirts, trousers, jackets, people, and more to efficiently train the machine learning model on multiple properties like price, color, design, and so on.
2. Healthcare Industry: To train machine learning models to identify deformities in human X-rays, the polygon technique can be used in annotating human organs in medical X-rays. This is one of the most important use cases, as it is altering the healthcare business by detecting diseases, lowering expenses, and boosting patient satisfaction.
3. Self-driving vehicles: Although autonomous driving has seen some success, there is still a long way to go. Many automobile manufacturers have yet to implement the technology, which uses semantic segmentation to recognize the road, cars, traffic, traffic signals, poles, people, and other objects in an image so that vehicles can be aware of their surroundings and detect obstacles in their path.
4. Detection of Emotion: Landmark annotation is used to measure the subject’s emotional state of mind at a certain piece of information by detecting human emotions/sentiments like happy, sad, angry, or neutral.
Areas which can improve by detection of emotion include product reviews, service reviews, movie reviews, email complaints, customer calls and meetings, and more.
5. Supply chain: Lines and Splines are used to designate warehouse lanes in order to identify racks based on their delivery position, which helps robots optimize their course and automate the distribution chain, reducing human interaction and errors.
How can GTS help you in Image Annotation?
Global Technology Solutions is an AI data-gathering company that offers datasets for your different machine learning projects. When it comes to artificial intelligence data collection, GTS is the leader. We have a team of seasoned experts who have a track record of success in numerous types of data collection. We have improved image, speech, video and text data collection systems. The data we provide is always checked for Dataset For Machine Learning, meaning we will provide the best data in terms of quality assurance.
The information we gather is used in the development of artificial intelligence and machine learning. We have data on various languages spoken all over the world because of our global reach, and we use it expertly. We handle challenges that artificial intelligence companies face such as machine learning problems and dataset bottlenecks. We provide this dataset seamlessly.
Comments
Post a Comment