Image Annotation

Intelligent Image Annotation Services

Pixelwise image annotation service for all types and formats of images making easier for computer vision to detect the varied objects just like humans.

Image Annotation

Image annotation is playing a significant role in computer vision, the technology that enables computers to gain high-level knowledge from digital images or videos and to see and understand visual information just like humans.

Think about when you were a child. At some point, you learned what a bird was. Eventually, after seeing many birds, you started to understand the different breeds of birds and how a bird was different from other species in the animal kingdom. Like us, computers need many examples to learn how to categorize things. Image annotation provides these examples in a way that’s understandable for the computer.

Image annotation is the mechanism of labeling images of a dataset to train a machine learning model. Therefore, image annotation is used to label the characteristics you need your system to recognize. Training an ML model with labeled data is called Supervised Learning.

The annotation task usually involves manual work, sometimes with computer-assisted help. A Machine Learning engineer establishes the labels, known as “categories”, and provides the image-specific information to the computer vision model. After the model is trained and brought into successful action, it will predict and recognize those established attributes in new images that have not been annotated yet.

Image Labelling is necessary for special activity datasets because it lets the training model detect the great important parts of the image are, that is, categories so that it can later use those ideas to identify those categories in new, never-before-seen images.

Depth Analysis on Image Annotation Types

Image annotation is often used for image classification, object detection, object recognition, image segmentation, machine learning, and computer vision models. It is the performance used to create quality datasets for the models to train on and thus is useful for supervised and semi-supervised machine learning models.

The purposes of image annotation (image categorization, object detection, etc.) require different executions of image annotation in order to develop successful datasets.

1. Image Categorization

Image categorization is a type of machine learning model that stipulates images to have a single label to identify the entire image. The image annotation procedure for image categorization models aims at recognizing the occurrence of similar objects in images of the dataset.

It is used to train an AI model to establish an object in an unlabeled image that looks similar to classes in annotated images that were used to train the model. Training images for image categorization is also referred to as tagging. Thus, image categorization aims to simply establish the occurrence of a defined object and name its predetermined class.

An example of an image categorization model is where different animals are “detected” within input images. In this example, the annotator would be provided with a set of images of different animals and asked to categorize each image with a label based on the specific animal species. The animal species, in this case, would be the categories, and the image is the input.

Providing the annotated images as data to a computer vision model trains the model for the unique visual characteristic of each type of animal. Thereby, the model would be able to classify new unannotated animal images into the relevant species.

2. Object Detection and Object Validity

Object detection or validity models take image categorization one step further to find the presence, location, and the number of objects in an image. For this type of model, the image annotation procedure requires boundaries to be drawn around every detected object in each image, allowing us to locate the exact position and number of objects present in an image. Therefore, the main difference is that categories are detected within an image rather than the entire image being categorized as one class (Image Categorization).

The category location is a parameter in addition to the class, whereas in image categorization, the class location within the image is irrelevant because the entire image is identified as one class. Objects can be annotated within an image using labels such as bounding boxes or polygons.

One of the most common examples of object detection is people detection. It requires the computing device to continuously examine frames to identify specific object features and recognize present objects as persons. Object detection can also be used to detect any aberration by tracking the change in the aspect over a certain period of time.

3. Image Segmentation

Image segmentation is a type of image annotation that involves segregating an image into multiple segments. Image segmentation is used to locate objects and boundaries (lines, curves, etc.) in images. It is processed at the pixel level, distributing each pixel within an image to a specific object or category. It is used for projects requiring higher precision in categorizing inputs.

Image segmentation is further divided into the following three classes:

  • Semantic segmentation depicts boundaries between familiar objects. This routine is used when great precision regarding the occupancy, location, and size or shape of the objects within an image is needed.
  • Instance segmentation identifies the presence, location, number, and size or shape of the objects within an image. Therefore, instance segmentation helps to label every single object’s existence within an image.
  • Panoptic segmentation combines both semantic and instance segmentation. Accordingly, panoptic segmentation provides data labeled for scenarios (semantic segmentation) and the object (instance segmentation) within an image.

4. Boundary Recognition

This type of image annotation indicates lines or boundaries of objects within an image. Boundaries may include the edges of a particular object or regions of terrain present in the image.

Once an image is properly annotated, it can be used to indicate similar patterns in unannotated images. Boundary recognition plays a noteworthy role in the safe operation of self-driving cars.

5. Annotation Shapes

In image annotation, different annotation shapes are used to annotate an image based on careful techniques. In addition to shapes, annotation procedures like lines, splines, and landmarking can also be used for image annotation.

Above are some popular image annotation procedures that are used based on the use case.

Bounding Boxes

In deep learning, bounding boxes are one of the most commonly used image annotation techniques. This approach will save resources and improve annotation performance as opposed to other image processing approaches. Annotators would be told to draw bounding boxes around entities like vehicles, pedestrians and cyclists within traffic images.

3D Cuboids

Building a 3D representation of the world from 2D images is one of the major concerns in Computer Vision and that is why we are here to help the industries with. Cuboid Annotation is the task of labelling objects in 2D images with cuboids. The 3D cuboids help to determine the depth of the targeted objects such as vehicles, humans, buildings etc.


In the era of data annotation, precision is the crucial aspect for the accurate results of your autonomous machine. Several types of data annotation can be applied as per the case. However, polygon annotation is the best way to ensure pixel-perfect precision. But it is imperative to have the right tools and trained staff for accurate training datasets.


This is used to recognize basic points of interest within an image. Such points are referred to as landmarks or key points. Landmarking is important in face recognition. Landmark annotation is used to detect small objects and shape variations by creating dots across the image. This type of annotation is useful for detecting facial features, facial expressions, emotions, human body parts and poses.

Lines & Splines

Lines & Splines Annotation is a type of annotation used when we have to make a particular shape recognizable by our ML/AI Models. We aim to provide the best quality annotation services for enabling the use of high-quality data for AI and machine learning. With the help of lines & splines annotation, it is easier to train vehicle perception models to detect the lane accurately while in motion.

Semantic Segmentation

Semantic Segmentation is the process of further classifying the pixels of the same object or label. With the help of our well-versed team, we provide an impeccable experience in terms of semantic Segmentation and labeling multiple classifications of an image along with pixel-wise annotation. We strive to deliver the most efficient and optimum result before the promised deadline at a very reasonable cost.

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