Why Accurate Image Annotation is Crucial for Machine Learning

 

Let’s begin with annotations.

Annotation can be described as labelling, tagging, or informing the machine learning what the given data is. For this instance image annotation refers to tagging or the process of labelling data on an image is called image annotation services. Machine learning systems can be trained using supervised systems to mark an image, letting the system know what you want it to recognize.

For us to get the desired results or data, the system must be trained, validated and tested on a large amount of training data, this is where image annotation takes place. Image annotation is mainly used for the identification of boundaries, objects, segments in an image and classification of objects, segments and characteristics contained can be labelled by the appropriate marking of boxes and labels.

The image annotation is mainly categorized into two topics:

1. Image classification: machine learning involves the process of teaching machines to recognize objects that are unlabeled images that look like objects in labelled images and train itself. Tagging basically will help the images to classify themselves properly. Ex: taking a house, the interior portions that are large can be labeled by “dining area”, “living area”, or “common room”. Outside of the house can be labeled by “swimming pool”, “backyard”, “garage” etc.
2. Object recognition / detection: One of the major fields in computer visions involving identification of objects in digital images or videos. This technology is applied to autonomous vehicles, robotics, security systems, augmented reality, etc. Overall, object recognition systems are a rapidly advancing field that identify, label and count one or more objects in an image, with many exciting applications and opportunities for innovation.

Machine learning and accuracy.

We know that machine learning is directly related to accuracy as it is one of the key elements that affects the performance of machine learning models. The main goal of machine learning is to establish a model that can accurately get the outcome of a given task, some examples are, classification of images, predicting customer behavior. One can even say that accuracy is defined as the percentage of correctly predicted outcomes out of all the predictions made by the model. Accuracy alone may not provide a proper complete picture of a machine learning model’s performance. Other metrics, like precision, recall, and F1 score, may vary according to specific task and the class balance in the dataset.

One of the most advanced ways of image annotations is segmentation. Segmentation is a process that is used to differentiate objects within the image on how similar or different they are. It can also be used to identify changes over time.

Segmentation has three main categories- Semantic Segmentation, Instance Segmentation, Panoptic Segmentation.

There are a number of factors that affect the accuracy of the image annotation. Classifying the objects in the images through boundary recognitions, boundaries can be the edges of an individual object or areas of locations etc. When images are annotated accurately, machine learning trains itself to recognize similar patterns in an un annotated image by using the other annotated images. This technique gives us the liberty to train the machine in a way where it distinguishes between the shelves of a store to the lanes in a store.

Image annotation company play an important role in the accuracy of machine learning. Some of them are bounding box, landmarking, masking, polygon, polyline, tracking, transcription. Using these techniques we can obtain a high level of accuracy and avoid most of the errors.

There are a number of factors that affect the accuracy of the image annotation. Classifying the objects in the images through boundary recognitions, boundaries can be the edges of an individual object or areas of locations etc. When images are annotated accurately, machine learning trains itself to recognize similar patterns in an un annotated image by using the other annotated images. This technique gives us the liberty to train the machine in a way where it distinguishes between the shelves of a store to the lanes in a store.

Image annotation techniques play an important role in the accuracy of machine learning. Some of them are bounding box, landmarking, masking, polygon, polyline, tracking, transcription. Using these techniques we can obtain a high level of accuracy and avoid most of the errors.

How GTS can help you?

Global Technology Solutions is a AI based Data Collection and Data Annotation Company understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text dataset, ADAS annotation and Video datasets are among the datasets we offer. We offer services in over 200 languages.