Cogito Tech

Image Annotation : overview 2022 used various type of annotation

Posted on July 13, 2022April 18, 2025 by Cogito Tech

One of the most crucial phases in creating computer vision and image recognition systems, which entails identifying, getting, characterizing, and evaluating outcomes from digital images or videos, is image annotation.

AI applications frequently employ computer vision, including security, medical imaging, autonomous cars, and others. As a result, image annotation is essential for developing AI/ML in many fields and sectors.

Before we dig deep into the topic, let us discuss some basic nuances of image annotation.

Image annotation

Adding labels to an image is known as an image annotation. These labels are set by the AI engineer and are selected to provide details about what is seen in the image to the computer vision model.

react to section with heart react to section with light react to section with money react to section with thumbs-down The number of labels on an image might be different according to the project. A single label will sufficiently convey the complete image’s information (image classification) for specific projects. Other applications need many items labeled with distinct labels inside a single photograph.

The Process of Image Annotation. How does it work?

Three crucial elements are required to annotate an image:

1- Images

2 – Adding annotations to the photos

3 – A tool for adding annotations to the photos

Finding and training annotators to complete the annotation tasks is the first step in most image annotation initiatives. Although AI is highly specialized, annotating AI training data is not always necessary. While a master’s degree in machine learning is required to build a self-driving automobile, it is not needed to draw boxes around cars in photos (bounding box annotation). As a result, few annotators hold degrees in machine learning.

However, because each organization will have distinct needs, these annotators should get in-depth training on the requirements and norms of each annotation project.

After receiving training on annotating the data, the annotation team will begin working on hundreds or thousands of photos on a platform specifically designed for image annotation. This platform’s software needs to be equipped with all the tools required for the particular kind of annotation to be done.

What methods and techniques exist for image annotation?

There are many different image annotation methods and techniques, but it doesn’t imply you should use them all. Understanding the most popular image annotation methods and techniques along with use cases will help you decide which annotation tool to use for your project and what it requires.

Bounding boxes

Rectangles are drawn around symmetrical items like furniture, vehicles, and packages using bounding boxes. This facilitates object detection and algorithm localization, which is essential for driverless cars.

Self-driving automobiles can navigate the roadways securely with annotations of people, traffic signals, and vehicles. Bounding boxes come in two and three dimensions (cuboids).

Segmentation

Segmentation goes beyond object identification and picture categorization. With this technique, a picture is divided into several parts, each of which is given a label. Since each segment consists of pixels, each pixel has a label, which increases the accuracy of the annotation.

Three categories of segmentation exist:

1 Semantic segmentation

Semantic segmentation involves grouping an image into clusters and giving each group a label. Consider that we have a picture of three sheep. Each sheep will be counted as a separate cluster for semantic segmentation. The sky will serve as the background, which will also be considered.

2 Instance segmentation

Objects’ existence, position, form, and number are determined through instance segmentation. For example, instance segmentation may count the number of persons in a picture. Let’s revisit the sheep illustration. Even though they are given the same name, each sheep will be counted as a different instance in the instance segmentation scenario.

3 Panoptic segmentation

Instance segmentation and semantic segmentation converge in panoptic segmentation. It semantically segments the image and categorizes each pixel, identifying the instances to which each pixel belongs (instance segmentation). Each sheep will be tallied independently in our case, even if all the pixels in the image will be given names.

4 Skeletal Annotation

Skeletal annotation draws attention to body alignment and mobility. Annotators use this technique to connect lines on the human body by adding dots at the locations where the lines articulate. For instance, a line from a point on the wrist to a point on the elbow is then connected to the shoulder, and so on.

This produces a condensed representation of a body’s location that computers can recognize. This approach is used for video in most sports use cases, requiring annotators to precisely identify body postures over tens of thousands of individual frames.

5 Bitmask Annotation

This annotation style permits gaps or disjointed annotations by connecting individual pixels to particular objects.

6 Polygons

The edges of objects with asymmetrical shapes, such as rooftops, plants, and landmarks, are annotated using polygons.

7 Key-points

By placing dots around the target item, such as facial characteristics, body parts, and stances, key points are used to annotate microscopic shapes and details.

To achieve certain annotation objectives, any of the aforementioned annotation approaches can be combined. AI firms have freedom and alternatives when organizing annotation projects as the company can also do customised annotations as per the requirements.

Essential applications and benefits of Image annotation

When employing computer vision (CV) models, image annotation is extremely important for machine learning and artificial intelligence.

Across sectors, the advantages and significance of picture annotation services are becoming recognized. In 2023, the market for AI and machine learning data solutions is predicted to reach $1.2 billion.

Here are some examples of how businesses in various industries use image annotation services to their advantage.

Transportation: CV is used by self-driving automobiles, which are a reality today. Advanced machine learning algorithms must power these vehicles to ensure their efficiency and safety. Automobile makers may create intelligent apps for these autonomous vehicles with picture annotation.

Healthcare: In healthcare, CV can assist increase the precision of the diagnosis and raise the standard of care. With image annotation, CV systems may use CT scans, MRIs, and other diagnostic technologies to focus on patterns and problems like tumors or hairline fractures.

Agriculture: Precision agriculture combines established farming practices with cutting-edge technology to increase profitability, production, and sustainability. CV systems make it feasible to forecast agricultural output, assess plant health, and improve soil quality. In modern agriculture, robotics, GPS sensors, and drones play significant roles.

eCommerce and retail: Annotating images may improve clients’ user experiences and aid them in selecting the best items. When an annotation is appropriately done, each item on the website will have precise descriptions and labels. Annotation ensures that the products are correctly categorized, improving search results. Additionally, it helps to improve visual search. react to section with heart react to section with light react to section with money react to section with thumbs-down

Conclusion

The goal of image annotation is to make the most of technology, and it is something that will never go away. When key AI projects need image annotation, most AI organizations use expert data labeling services.

react to section with heart react to section with light react to section with money react to section with thumbs-down Businesses that provide expert data labeling services, like Cogito or Anolytics, appen, or any other, have skilled experts to help with data labeling and annotation. These companies can tailor their services to your unique needs as they provide seamless project management, quality control, and lower overheads. Originally published at – https://hackernoon.com/image-annotation-what-no-one-is-talking-about-in-2022

Big data analytics and AI importance in media and entertainment industry

Posted on May 22, 2021May 22, 2021 by Cogito Tech

The media and entertainment industry is also utilizing the power of Artificial Intelligence (AI) in making the visual content more interactive and interesting. It is helping to serve the audience a data-intensive and personalize automated content making their viewing experience more interesting and entertaining.

While on the other hand, the entertainment industry needs such innovative technology to make the audience experience more enjoyable and user-friendly. AI companies are also consistently working to integrate this technology into various sub-fields of the entertainment sector improving its efficiency.

AI Applications in Media and Entertainment

The application of AI in media and entertainment is witnessing rapid growth especially in terms of distributing and showing visual content. Media companies face challenges while developing the content in high quantity while ensuring the quality, hence they adopt AI to achieve this objective.

And once they achieve their objective, they improve their services enhancing the experience of the customers. So, we have discussed here the use of AI in the media and entertainment industry with use cases and examples.

Personalizing the Content & Recommendations

From music app to OTT platforms, the audio, as well as visual contents, can be personalized as per the preferences and previous experiences. Using machine learning, users behavior and demographic details, music or videos are recommended like what kind of movies they like most.

AI using advanced machine learning algorithms and deep learning networks helping in delivering personalized content to users. And this kind of highly personalized experience for users helps media or entertainment companies increase their user base and offer a better service compared to their competitors.

Online Advertising with Targeted Audience

Online advertisements in the media industry are playing a significant role in branding and business promotions. And AI is used to make online advertising more precise and productive with a targeted audience for higher conversion rates.

The best example is Google Adsense and Adwords that can use the user’s history like what kind of products they were searching for or browsing on web or eCommerce sites. And this kind of AI-based sensing helps AI to show the ads as per the user’s preferences. It helps advertisers to target the right audience and get maximum outputs from the Ads.

Controlling the Online Content Broadcasting

The online media and entertainment industry is running with the help of different types of content. And apart from general topics or common subjects, there is objectionable content broadcasted through TV, Online Music Channels or OTT platforms.

Controlling such content is mandatory from the regulatory authorities. Again AI helps here in detecting and filtering such objectionable contents. AI can identify the user’s age and gender before showing such content or using the automated content moderation service to moderate the objectionable content before broadcasting with the audience category ratings like suitable for children or only for adults.

Classification & Categorization of Content

Online streaming platforms like YouTube or OTT have different genre of music videos, songs, movies or TV shows. And these online platforms are using AI algorithms to show the content as per the user’s preference and choice.

The AI-enabled system can detect the objects, data, visual scenes and other details about the movies like genre, cast and crew members to show them similar content. Implementing the AI in media and entertainment industry can automate the categorization and classification of content for a better users experience.

Meta Tagging Subtitles & Automated Transcription

Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.

Apart from that movies, music videos and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is converted into different languages with subtitle and audio annotation to make it understandable to a wider populace for more engagements of the users. Powered by Cogito Blog

What is Data Annotation?

Posted on March 22, 2021July 13, 2022 by Cogito Tech

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 data with 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.

Which is better for image classification, supervised or unsupervised classification?

Posted on August 26, 2020January 14, 2025 by Cogito Tech

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 Image Data 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.

What is Best Data Labeling Process to Create Training Data for AI?

Posted on August 24, 2020April 18, 2025 by Cogito Tech

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.

What is Healthcare Training Data? Why is it important?

Posted on June 26, 2020April 19, 2025 by Cogito Tech

AI and machine learning models developed for healthcare sector or medical treatments and care, need the healthcare training data to train such AI models. And without healthcare training data it is impossible the train the AI model mainly through supervised machine learning. And computer vision based models need the annotated images to detect things learnt through algorithms.

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How to Data Sets Annotated for Sentiment Analysis in the News Headlines?

Posted on June 24, 2020April 28, 2025 by Cogito Tech

To make understandable the sentiments of the people by the AI model, a huge amount of training data sets required for machine learning. And language annotation is the right process to annotate the key texts in a document and make it comprehensible to machines.

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Are There any Content Moderation Companies in India?

Posted on June 23, 2020March 18, 2024 by Cogito Tech

Yes, of course, there are many companies in India providing content moderation services. But finding the best one is a little difficult task for anyone to moderate the content with quick and right action. Actually, different types of content should be monitored and analyzed carefully to filter the same into the right category to remove from the online platforms.

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Hire Machine Learning Engineer and Data Scientist for AI Models Development

Posted on June 16, 2020 by Cogito Tech

To develop the AI models machine learning engineers required with lots of training data sets. And to analyze the data for machine learning and AI, data scientists are required. Hiring the right data scientist and machine learning engineers are difficult for the AI companies. And if the project is on temporary or contract basis a suitable machine learning engineer is required for AI developments.

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