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 actually fundamental task that helps to classify and comprehend the entire 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 to images in which only one object appears and that is only 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 that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.
The classification is the process done with multi-step workflow, while, the Image Classification toolbar has been developed to provide an integrated environment to perform classifications with the tools.
Not only does the toolbar help with the workflow for performing unsupervised and supervised classification, 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, capability to analyze the quality of the clusters, and access to classification tools
In unsupervised classification, it first groups pixels 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 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.
While 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 label 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 to make the objects recognized with the better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. And depending on the AI model or machine learning algorithms compatibility, image classification process is followed to classify the images with better accuracy and quality object detection.
Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings.
Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model.
During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients. The data is encrypted to make sure it can be safely delver to the clients to avoid any risk. So, right here we will discuss about the data labeling process to step wise facts.
DATA LABELING PROCESS
Collection of Datasets
The first step towards data annotation is understand the problem to provide the precise training data. Hence, collecting the datasets from client is an important aspect. So, the raw data is collected directly from the client in the well-organized format.
The data is collected through a proper channel to make sure its originality and security. Many business enterprises follow the different routes to send the data for labeling. Sometimes it is supplied in encrypted format and after data annotation it is again sent to client in the secured format.
Labeling of Dataset
After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for the supervised machine learning labeled data is required, and proper labeling is important to make sure AI model get trained precisely and work in the right manner.
Choosing the right tools and technique is another factor for data labeling. And in image annotation is done to create the training data sets for computer vision based AI model. The quality is also need to be ensured to make sure the model can predict with the accurate results. To consider all these points two points also need to 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 segmentation of the object. Here if client provides the specific tool or software, then annotators use to annotate the images using the same.
Once the data sets are assigned to annotators and instructed what type of annotation and what are the tools will be best suitable to annotate the data.
Who Will Label the Data: Similarly, the next step into data labeling process comes, who will annotate or label the data. Here, two options are available for the AI companies – first organize the in-house data labeling facility which could be easy control for you and might cost less but it can take extraordinary time due to collection and labeling of entire data sets.
The second option is outsource the labeling task to other data annotation companies, who have 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 data has the ability to aggregate quickly. While on the other hand transparency, accuracy and high-cost are the concerning factors with outsourcing services.
Quality Check and Evaluation
After annotating the data, checking the quality is one of the most important factors of data labeling process. Here, qualified annotator manually check the quality of each annotated images to make sure machine learning algorithm get trained with right accuracy.
Here, the data sets are also evaluated to validate the same, and if there is any correction the data is annotated correctly and finally validated for machine learning training. Here highly experienced, annotators are required to prudently the check the quality of data labeled to make sure AI companies get the best and high-quality datasets at 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 is ensured till the data is delivered to client. And the mode of delivering the data also depends on the company to company but there should be safe mode to send such data with complete confidentiality and safety.
Data Labeling Process at Cogito
Most of the companies follow the above discussed data labeling process but few companies have more complex or even more sophisticated but secured data annotation process. Cogito is one the companies providing the world-class data labeling solution with next level of accuracy. It is following the international standards for data security and privacy to ensure the originality of AI model.
Data labeling process for image annotation is not only critical but time taking. It creates training data sets for machine learning and AI model development. And the cost of such training data also depends on cost labeled data available for such needs.
Video annotation to label the moving objects in the video recording to make it recognizable to computer vision based machine learning algorithms. Self-driving cars or autonomous vehicles needs such highly labeled videos with right metadata helping such machines to detect the physical object through various sensors and move safely.
Annotation services is the kind of data labeling service offered by professional companies to make data like texts, images and videos understandable to machines. Basically, it is the process of making the data recognizable to machines generally through computer vision (in image and video annotations).