The three most important fields in Image artificial intelligence are: algorithms, computing power and data. With the continuous implementation of artificial intelligence applications, accurate data annotation has become an extremely critical factor affecting the implementation of AI. Image annotation is a kind of data annotation. Base label type.
What is Image Annotation?
Image annotation is a process of labeling image features using artificial or AI technologies. Image annotation is one of the important tasks of computer vision. Image annotation is the process of attaching labels to images, which can be a part of the entire image. label, or multiple labels for each group of pixels in the image. These labels were predetermined by AI engineers and selected to provide the computer vision model with the information shown in the image.
The process of image annotation
The main process of image annotation is divided into three aspects: data cleaning, data annotation, and annotation inspection. The first is data cleaning, which is to screen quality problems such as missing values, noise data, and duplicate data in the data, and then perform data annotation to divide Annotation tasks are carried out according to the established annotation specifications, which can classify the semantic content of images well, make annotations separate from low-level features such as color and texture, and realize functions such as image retrieval from high-level features such as semantics, and finally Labeling inspection is to review the labeling quality by labeling auditors.
Image annotation type
The commonly used types of image annotation are: classification annotation, line annotation, point annotation, bounding box annotation, pixel annotation and so on.
Classification labeling is one of the most basic labeling methods, and its form of expression is generally that a picture corresponds to a digital label.
Line annotation is widely used in the field of automatic driving to identify lanes and boundaries. The advantage of this method is that the pixels on the line do not need to be continuous, which can effectively detect lines with interruptions in the image or partially occluded objects.
Point annotation is usually used in scenes with relatively detailed image features, such as estimation of human body pose, recognition of facial features, and so on.
The bounding box annotation is mainly used for object detection, which is used to define the specific position of the object in the image. It can be subdivided into 2D bounding box and 3D bounding box. The 2D bounding box is fast and easy to label, but it cannot provide some specific important information, such as the direction of the object. The 3D bounding box label solves the problem of object direction. When the object is occluded, the 3D bounding box label can be imagined by itself The dimension of the bounding box.
Pixel labeling, also known as region labeling, is a labeling method that classifies pixels in an image, mainly including semantic segmentation and instance segmentation. Semantic segmentation is a machine learning task that requires pixel-level annotation, where each pixel in an image is assigned a class, making each pixel carry semantic meaning. Instance segmentation is a subtype of image segmentation that identifies each instance corresponding to each object in the image at the pixel level. Both instance segmentation and semantic segmentation are one of two levels of granularity for image segmentation.
Application fields of image annotation
A common application of image annotation is face recognition, which includes extracting relevant features from face images to distinguish people and human objects in the image, and using image annotation technologies such as key points and landmarks to point to objects through corresponding trajectories. Different points on different parts of the face are tracked, which strengthens the effectiveness of the face recognition algorithm.
Image annotation technology is also applied in various tasks in the agtech industry, detecting plant diseases by identifying images of healthy crops and viruses, which can be achieved by using semantic segmentation or bounding box types, which is the image annotation in agtech industry One of the most basic applications.
Image annotation is also used in security systems to divide video regions into restricted and non-restricted areas through semantic segmentation, and image annotation can also be used to detect somewhat suspicious activities.
Image annotation is also used in robotics to help robots distinguish between various objects in their surroundings.
The Future of Image Annotation
Data labeling is an emerging profession that has emerged in recent years to help artificial intelligence training data. It mainly labels images, text, and sounds in different ways according to different task requirements. Most learning models require a large number of training sets. Only with the support of data can better results be achieved. With the continuous development of technology, the demand for data in many fields such as smart driving, smart home, and smart robots is also extremely large. As the basis of artificial intelligence, data will also be used in the future. will play an extremely important role.