Making the data understandable to machines by labeling using the certain techniques like outlining or shading the text or objects images is basically known as data annotation. The data can be anything from simple text to images or videos and audios available in various formats.
The main motive of data annotation is highlighting the important words, texts or objects using the annotation techniques to make it recognizable to machines or computer vision used for machine learning or artificial intelligence model developments.
Types of Data Annotation
There are different types of data annotation services offered by companies providing machine learning and AI training data. Text Annotation, Image Annotation, Audio Annotation, Video Annotation are the leading types of annotation services you can find in the market. And under each types there are certain annotation techniques like image annotation bounding boxes, cuboid, semantic, polygons and point or polylines are the popular annotation methods.
Data Annotation for AI and Machine Learning
Apart from few human oriented needs, most of the annotation is done for machine learning and AI data training. Data annotation techniques helps the machines to recognize the actual dimension, shape, size and types of content available on the web based services. Each data has its own format and most of them are understandable to humans but to make such data comprehensible to machines a precise data annotation services is required.
How to Get Annotated Data for Machine Learning or AI?
Developing the machine learning or AI models not only required special skills or knowledge but having a high-quality training data is also important to make such models functional and give the accurate results. Getting the quality training data with right annotation and labeling is difficult, especially if you are looking for free data annotation service.
Cogito is one the well-known companies offers the high-quality machine learning training datawith proper annotation and data labeling. It is expert in image annotation and data labeling service for various industries like healthcare, automobiles, retail, agriculture, ecommerce, banking finance and information technology fields with well-diversified clients portfolio.
Image classification is a fundamental task that helps to classify and comprehend an image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.
Usually, Image Classification refers to images in which only one object appears, and that is the only object analyzed. In contrast, object detection involves both classification and localization tasks and is used to analyze more realistic cases in which multiple objects may exist in an image.
Supervised vs Unsupervised Classification
Supervised classification is based on the idea that a user can select sample pixels in an image representative of specific classes and then direct the image processing software to use these training sites as references for classifying all other pixels in the image.
Classification is a process done with a multi-step workflow, and the Image Classification toolbar has been developed to provide an integrated environment for performing classifications with the tools.
The toolbar not only helps with the workflow for performing unsupervised and supervised classification, but it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.
Supervised Image Classification
Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the ImageData Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.
In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.
For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.
Unsupervised Image Classification
Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, the capability to analyze the quality of the clusters and access to classification tools
In unsupervised classification, pixels are first grouped into “clusters” based on their properties. Then, you classify each cluster with a land cover class.
Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.
Which one is better?
No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of the area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.
On the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and labeling the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.
Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better for making the objects recognized with better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. Depending on the compatibility of the Gen AI model or machine learning algorithm, an image classification process is followed to classify the images with better accuracy and quality object detection.
Data annotation is one of the most crucial processes in the AI world. It makes the set of training data available for machine learning algorithms. A computer vision-based AI model needs annotated images to make the various objects recognizable for a better understanding of the surroundings.
The data annotation process involves collecting data, labeling it, performing quality checks, and validating it, which makes the raw data usable for machine learning training. For supervised machine learning projects, it is not possible to train the AI model without labeled data.
During the whole process, well-trained human power with the right tools and techniques annotates data as per the requirements and then processes it in a highly secure environment for clients. The data is encrypted to ensure it can be safely delivered to clients to avoid any risk. So, right here, we will discuss the data labeling process step-wise facts.
DATA LABELING PROCESS
Collection of Datasets
The first step towards data annotation is understanding the problem to provide precise training data. Hence, collecting the datasets from the client is an important aspect. So, the raw data is collected directly from the client in a well-organized format.
Data is collected through a proper channel to ensure its originality and security. Many business enterprises follow different routes to send the data for labeling. Sometimes, it is supplied in an encrypted format, and after data annotation, it is again sent to the client in a secured format.
Labeling of Dataset
After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for supervised machine learning, labeled data is required, and proper labeling is important to make sure the AI model gets trained precisely and works in the right manner.
Choosing the right tools and techniques is another factor for data labeling. Image annotation is done to create the training data sets for a computer vision-based AI model. Quality also needs to be ensured to make sure the model can predict accurately. To consider all these points, two points also need to be discussed here—how to label data and who will label the data.
How to Label Data: After getting the data set for labeling, the annotation team has to decide the type of annotation applied here, like detecting, classifying, and segmenting the object. Here, if the client provides the specific tool or software, then annotators use it to annotate the images using the same.
Once the data sets are assigned to annotators, they are instructed on what type of annotation and what tools will best suit the task.
Who Will Label the Data? Similarly, the next step in the data labeling process is who will annotate or label the data. Two options are available for AI companies: first, they can organize an in-house data labeling facility, which could be easy to control and might cost less, but it can take extraordinary time due to the collection and labeling of entire data sets.
The second option is to outsource the labeling task to other data annotation companies, which have a team of well-trained and experienced annotators to label the data for machine learning with better efficiency and quality. The best part of outsourcing is that data can be aggregated quickly. On the other hand, transparency, accuracy, and high cost are factors that concern outsourcing services.
Quality Check and Evaluation
One of the most important factors of the data labeling process is checking the data’s quality after annotating it. Here, a qualified annotator manually checks the quality of each annotated image to ensure that the machine-learning algorithm is trained with the right accuracy.
Here, the data sets are also evaluated to validate them, and if there is any correction, the data is annotated correctly and finally validated for machine learning training. Highly experienced annotators are required to prudently check the quality of data labeled to make sure AI companies get the best high-quality datasets at the best pricing.
Final Delivery of Annotated Datasets
The last step in data annotation process is after labeling, the data need to be safety delivered to client. Here again, the authenticity and privacy of data are ensured till the data is delivered to the client. The mode of delivering the data also depends on the company to the company, but there should be a safe mode to send such data with complete confidentiality and safety.
Data Labeling Process at Cogito
Most companies follow the above-mentioned data labeling process, but few companies have a more complex or even more sophisticated but secured data annotation process. Cogito is one of the companies providing a world-class data labeling solution with the next level of accuracy. It follows international standards for data security and privacy to ensure the originality of the AI model.
Cogito is the industry leader in data labeling service and annotation services to provide the training data sets for AI and machine learning model developments. All types of AI and ML services requires the training data for algorithms with next level of accuracy making AI possible into various fields like healthcare, retail and automotive and robotics etc.
Apart from AI and ML training data sets, Cogito is also render the various other services like Data Collection & Classification, Audio Video Transcription and Contact Center Services to wide range of industries with affordable pricing. It is basically involved in image annotation services at large scale with team of well-qualified and trained annotators for different types of projects from different fields to give quality results.
Services Offered by Cogito:
Visual Search
Image Annotation
Content Moderation
Sentiment Analysis
Data Collection
Data Classification
Search Relevance
Audio Transcription
Video Transcription
OCR Transcription
Machine Learning
Virtual Assistant
ChatBot Training
Healthcare Training Data
Contact Center Services
The services offered by Cogito is specially for the AI and ML companies in USA, Canada, UK and other countries in Europe and other continents. It is one of the best annotation service provider in the industry and annotating images under the world-class working environment to deliver each project timely while ensuring the customize requirements and budget of the customers.
Image annotation is becoming the need of AI industry to train the machines with large volume of visual data sets. It is a kind of object lebelling in images making recognizable for computer vision that helps machine learning algorithms to understand the similar objects. And there are various types of image annotation techniques applied while doing this job.
