Image annotation for deep learning and computer vision

It is an era of artificial intelligence, and image annotation is the foundation behind many artificial intelligence products you interact with. Think of when you were young and learnt what a cat was. Eventually, after seeing many cats, you started understanding the different breeds of cats and could differentiate them from other animals or objects.

Similarly, computers need many examples to learn how to recognize objects, and image annotation provides these examples in a way that is understandable for the computer version.

What is image annotation?

In machine learning and deep learning, image annotation is the process of data labelling or classifying an image using text, annotation tools, or both to mark the features you want your machine learning system to recognize. When you annotate an image, you add metadata to a dataset called tagging, transcribing, or processing.

Simple image annotation may involve labelling an image with a phrase that describes the objects pictured in it, while complex image annotation is generally used to identify, count, or track multiple things or areas in an image. Image annotation takes a significant amount of data to train, validate, and test a machine learning model to recognize objects and boundaries and segment images, such as meaning or whole-image understanding.

The different techniques of image annotation required to develop effective data sets are:

Image classification- image classification is a type of machine learning model used to train an AI model to identify an object in an unlabeled image.

Object detection and object recognition- object detection or recognition models take an image classification step further to find the presence, location and the number of objects in an image and are used to detect any anomaly by tracking the change in the features over a certain period of time.

Image segmentation involves partitioning an image into multiple segments to locate objects and boundaries in images and is often used for projects requiring higher accuracy in classified inputs.

Boundary recognition- boundary recognition identifies lines and boundaries of objects, including the edges of a particular object or regions of topography present within an image.

When do you need image annotation for the computer version?

To train and develop computer version algorithms, data annotation is needed, specially where pre-trained models are not specific or accurate, like —

When a new task comes up

Image annotation is necessary when artificial intelligence is applied to new AI tasks without appropriate annotated data; like in industrial automation, a computer version is frequently used to detect specific terms and conditions.

When the data is restricted

While there is plenty of data available on the internet, some image data requires a license agreement, and One may restrict its use for the development of commercial computer version products like in medical imaging; manual data annotation generally comes with privacy concerns when sensitive visuals are involved.

Business image annotation solution

The computer version platform include a built-in image annotation environment and provide an integrated image and video annotation solution for professional teams.

With their expert in house team, they are known to deliver over 50MM datasets a month and are skilled at drawing bounding boxes, cuboids, polygons, text annotation, image masking, image annotation and tagging, data annotation and labelling, contour annotation, tagging of aerial view pictures, 2D and 2D annotation, segmentation, etc. for various industries.

If you are looking for an Outsourcing company for your AI annotation and Data labelling Services and need a professional image annotation solution, check out Infosearch BPO services. Write to us to discuss your requirement — enquiries(AT)infosearchbpo(DOT)com



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