Image annotation plays a superficial role in Artificial Intelligence and acts as an integral part of the primary tasks of computer vision technologies. For example, Annotated Images are used to train machine learning or computer vision models for visual containing object recognition.
Indika is creating success stories for various companies by offering image annotation services with complete scalability. Our annotation process includes tagging and sorting images into different categories with the correct classification. We use high-quality tools and techniques to annotate images and fulfill our customers’ needs by delivering a custom service at affordable prices.
What is Image Annotation?
The process of annotating an image with labels and storing them in a dataset to train a machine learning model is called Image Annotation. It is part of supervised learning as training the model with labeled image data. Labels are the information given by the data engineer based on what is shown in the image.
Typically the image annotation task involves manual work, sometimes with computer-assisted help. First, a Data engineer predetermines the labels, known as “classes”, and feeds the image relevant to the computer vision model. Then, after the successful model deployment, it will recognize and predict those predetermined labels in new images that have not yet been annotated.
Why is Image Annotation needed?
For functional datasets, image annotation is necessary because it trains the models to predict the critical parts of the images called classes to use those notes in the future to identify those classes in new, never-before-seen images.
How to Annotate Images?
- Step1: Image dataset preparation.
- Step2: Specify the class and provide labels of objects to detect.
- Step3: Inscribe the object the client wants to detect in a rectangular box.
- Step4: Specify the class label for each box drawn.
- Step5: Put the annotations in the needed format.
As one of the leading image annotating startups, Indika has access to the most advanced tools. Our experts are proficient in dataset creations that highlight the environment where the customers will be used. In addition, we provide many image annotation services for computer vision and machine learning models.
- Bounding boxes
As the name suggests, we need to bound our image with a rectangular box to specify our target in this technique. Annotators choose bounding boxes when the shape of the target object is simpler defined. This technique is preferable in self-driving cars to ensure safety by detecting other cars, safety boards, and signboards.
2. Polygon Annotation
A higher level of precision for image annotations is offered by Polygon segmentation. The edges of objects are marked by putting dots and drawing lines on them. Discarding the unnecessary pixels is critical when it comes to irregularly shaped objects.
3. Semantic Segmentation
Semantic segmentation is used to analyze the visual content of images and determine how objects in an image are different or the same. This method is applicable when we want to understand the presence, location, size, and shape of objects in images.
4. Image Classification
In Image Classification, Annotators identify the presence of similar objects shown in images throughout a data set. For example, this annotation is used to train a model to identify an object in an unlabeled image similar to an object in other labeled images that are used to train the machine
Why Indika for Image Recognition Service?
Indika provides a fast scale image annotation to build a high-quality dataset for Machine Learning and Computer Vision models. Our AI and Data Science professionals help annotate images using a sequence of manual processes and high-end technologies. In addition, we provide free POC service for complete project discussion and to know our customer’s requirements accurately.