18Nov

Frequently Employed Text Annotation Techniques For Natural Language Processing

Artificial Intelligence and Machine Learning are now part of everyday life. The consequences of these new technologies have affected how we see and interact with the world. AI and ML applications have limitless potential to radically change and drive the global economy forward. These algorithms are opening new frontiers in medicine, the arts, and finance. Natural Language Processing is at the front of all these.

Recent breakthroughs in NLP mean that people with speech impairments can conveniently communicate using automatic voice recognition software. However, to realize this technology data must be carefully annotated to train the AI models. Otherwise, all the hype around AI and ML would be wishful thinking.

To adequately train an NLP model, massive amounts of annotated texts are necessary. Below is a breakdown of the different types of text annotation tools for computer vision NLP.

Entity Text Annotation

Crucial to training chatbots, entity annotation is the foundation block for training logical NLP solutions. Recognizing, fragmenting, and annotating values is referred to as text mining.  

Entity Linking

This refers to connecting similar entities to larger data repositories. This process is crucial to creating a logical NLP computer vision model.

Sentiment Annotation

Sarcasm is one of our natural reactions as humans. When giving reviews, we sometimes opt to be sarcastic whenever confronted with a bad experience at a spa or a hotel. A poorly trained software might understand sarcasm as genuine praise when it’s the complete opposite.

To avoid this, sentiment annotation/analysis is crucial. Judging from a person’s emotion or tone, people are able to label each sentiment as either positive, negative, or neutral. 

Linguistic Annotation

This is also referred to as corpus annotation. This refers to tagging language data in both text and audio recordings. Labelers are tasked with identifying and highlighting phonetic, grammatical, and semantic features in both audio and text. 

Intent Annotation

Intent annotation is mainly employed to decipher a user’s intention. Different users have different intentions when interacting with chatbots. Some users wish to learn about their overhead charges, others want statements, etc. This annotation technique uses different labels to categorize a user’s intent.

Conclusion

With this blog, we hope you have a better understanding of text annotation and how it is employed for computer vision. Text annotation can prove to be overwhelming when faced with ever-increasing data volumes. 

To cure this, it’s always wise to outsource this non-core yet important part of AI/ML model development. 

At Impact Outsourcing, we provide a professionally managed workforce backed by years of experience in data annotation. Contact us today and let us power your next NLP computer vision model.

19Oct

Outsourcing Image Annotation: A How-To Guide

The real-life application of facial recognition for security, autonomous vehicles, and even robot assistants is no longer restricted to the sci-fi movie realm. These life-altering technologies are already here and they are bound to shape our future in a major way. Computer vision AI applications are ever leading us in this direction. 

To actualize successful AI and ML applications, models rely on accurately labeled/tagged data. For instance, in order to build a computer vision application, massive loads of visual data must be annotated and fed into the model. This is what is referred to as image annotation. This human-powered task of labeling images can be tedious, overly expensive, and time-consuming.

Employing an in-house data annotation team can be a monotonous task that comes with its own set of challenges. As a consequence, we find that many businesses prefer to outsource some if not all of their data training needs. These include; image annotation, data collection, data validation, live project monitoring, etc.

Advantages of Outsourcing Data Annotation

Scalability

With a reliable image annotation outsourcing team, you rid yourself of the constraints that come with data volume upheavals. One can easily ask the outsourcing firm to scale up or down depending on your current needs.

Expertise

Data labeling companies come with a breadth of experience that places them in a unique position. They can better advise on the right talent, tools, and approach that fits your project. 

Saves Time

Data labeling and collection consume a huge amount of time and it takes even longer to train a team to do the job. By partnering with an experienced outsourcing company, the task of recruiting and training the team is passed on to them. This frees your time which can be better utilized in other aspects of running your company.

There are some very important points to go through before settling on a data annotation outsourcing partner. With the ever-increasing number of image annotation outsourcing companies, choosing the right fit can be a daunting task. 

Follow these steps to find your way through the murk.

Step 1: Realize your needs

For every computer vision application or model, there is a specific annotation technique to actualize it. You must first determine what your AI model use case is and the problems it intends to solve.

Below are some questions to ask yourself when selecting the right vendor.

  • What sort of data are you operating?
  • What sort of image annotation fits your project? (text annotation, image annotation, video annotation, etc)
  • What is your budget?
  • How do you determine project efficiency?

Being knowledgeable about your needs places you on a solid footing to effectively pass on your requirements to potential partners.

Step 2: Go for the right vendor

Selecting the right partner can make or break your AI/ML project. Below are some questions to help you select the right outsourcing partner. 

 

  • Industry Knowledge and Experience – Given the different types of annotation (image, video, text, etc), annotating data can vary depending on the type of annotation needed. Let’s say your AI model requires video annotation, be careful to select an outsourcing company with relative experience on the same before committing.

 

  • What platforms/tools do they employ – There are many annotation tools and platforms out there in the market. It is important to interrogate every potential outsourcing partner’s knowledge on this as they can advise on the best tool that meets your needs. 

 

  • Are they committed to ethical AI and Social Impact – Since you are basically offshoring your work, you want to ensure that you are making the most positive impact on the people that handle your project. Enquire on how annotators are remunerated and their overall benefits. From experience, most outsourcing companies are happy to share this information with a potential partner. 

 

Step 3: Monitor and Manage Expectations

To ensure the success of outsourcing data annotation, proper quality assurance is paramount. The outsourcing company must have layers upon layers of quality checks to guarantee high-quality datasets.

Measure the vendor’s ability to produce high-quality datasets by posing questions like:

 

  • Project Trial – Most outsourcing companies offer a free trial for clients to measure their quality and overall professionalism. Before committing to anything long-term, first, send the potential vendor a sample of the expected work and judge their output. If the quality satisfies your needs, then you can proceed to partner.

 

  • The number of Annotators/Capacity – This is important to ask for when you want to scale your team. You don’t want to commit to a vendor who can only commit a small number of annotators. Equally important, always go for the vendor who can easily scale down the team when the circumstance calls for it.

 

  • Pricing – It’s important to find out the most suitable pricing model for a successful partnership. This can be on a per-hour basis or per task/image. Depending on which one suits you best, always make it clear to the potential vendor.

Impact Outsourcing prides itself on providing humans in the loop, crucial for actualizing Artificial Intelligence and Machine Learning. We seek to create long-term meaningful employment for thousands of marginalized youth and women through data annotation jobs. With our years of experience in data collection, data curation, data labeling, and live project monitoring, we have birthed a quality-first attitude to project management. Try us today and we’d be happy to be your number-one outsourcing partner.

15Jul

Data Annotation In Agriculture: Using Artificial Intelligence and Machine Learning On Agricultural Data

In this new and modern digital age, artificial intelligence is being incorporated into vital human activities creating more efficiency and enhanced productivity. Not to be left behind, the agricultural sector has made great strides through AI-powered computer vision models in crop monitoring and production.

The role of AI robots, drones, and other automated machines is becoming ever more vital in planting, health monitoring, harvesting, and enhancing crop productivity. But have you ever sat back and asked yourself how these AI-powered machines aid in meticulous agriculture and farming? 

Surprisingly, AI-powered computer models are made possible through computer vision technology. To achieve this, the AI models are thoroughly trained using annotated/labeled images fed to them using the correct machine learning algorithms.

Image Labeling for Machine Learning in Agriculture

Image labeling services in the field of agriculture aids in performing different actions e.g. identifying crops, fruits, vegetables, or weeds. Once enough annotated data is uploaded onto the deep learning algorithm, an AI-powered computer model becomes intelligent enough to predict and perform human functions like sorting fruits and monitoring crop health.

Through the data labeling process, image and data annotation is playing an ever-increasing role in applying artificial intelligence and machine learning to agricultural data. Below are some of the ways in which image annotation is applied to machine learning for use in agriculture. 

Precise Agriculture using Robots

Robots are steadily emerging as the number one preference for farmers on farm fields. In agriculture, AI-powered robots are performing different actions with aid from machine vision algorithms. These robots can perform actions such as; plowing, planting, weed detection, and monitoring crop productivity and overall health. They also aid in picking fruits and vegetables, sorting, and packaging them accordingly. In addition to this, robots use computer vision cameras to the group and classify different farm produce faster and with improved accuracy.

With the aid of deep learning algorithms, it is easy for an AI-powered robot to identify faults from multiple angles using color and geometrical variations. The deep learning algorithms work by first identifying and locating the fruits and then moving to classify them appropriately.

In order to equip AI-powered algorithms, accurately annotated images of plants, crops, and floras are fed into the models. Through bounding box annotation services, AI-powered robots are trained to recognize and detect different crops, weeds, fruits, and vegetables. 

Sorting Fruits and Vegetables

Once all the fruits and vegetables have been collected, the task of sorting is done by AI-powered robots to separate the healthy from the rotten fruits and vegetables. Through the use of training data, accurately labeled images based on deep learning are used to sort and grade farm produce. 

Likewise, AI robots are able to sort flowers, stems, and buds of various breeds, shapes, and sizes. These models are trained to be at par with the strict international rules and standards of the different crops, fruits, and flower markets. 

Monitoring Soil, Crop, and Animal Health

Through the application of Geosensing technology, drones and other autonomous flying objects are able to determine the health condition of both crops and soil. This aids in informing farmers on what is the right time for sowing and what actions ought to be taken to save vulnerable crops. To maximize crop yields, the correct soil conditions and timely insecticides are key. In addition, AI-powered technology makes it easier to highlight the health of both crops and animals.

 

The health of animals is typically given by veterinarians. This is due to the fact that animal health often dictates the animal’s reproductive health, milk production, and feed intake making livestock rearing ever more profitable.

Crop Yield Forecasting Using Deep Learning

By applying AI in agriculture using deep learning data sets, predicting the expected crop yield using smart devices to analyze the data. 

Developing deep learning platforms needs in-depth knowledge to facilitate the training of accurate and reliable predictions. To train these algorithms, ample amounts of accurately annotated data are needed to develop such computer models.

AI in Forest Management

Artificial Intelligence is used in forest management by utilizing areal images taken by planes, satellites, and drones. Images captured by the said sources provide the raw training data to be later annotated/labeled. 

When Machine learning models are trained using accurately annotated data, the models are able to better detect illegal activities like tree cutting which eventually leads to deforestation damaging the Ecosystem. Assessing the growth and overall health of trees using AI models equips forest management stakeholders to make informed and better decisions when monitoring forests.

Image Labeling in Deep Learning for Agriculture

Getting a hand on top quality training data for machine learning is no mean feat for Machine Learning companies who work to develop AI models. But this Goliath of a challenge is made simpler with the help of data annotation companies like Impact Outsourcing. 

We assist AI companies to annotate and labeling training data for computer vision at a fraction of your initial cost while maintaining the same high-quality levels as expected.

Impact Outsourcing is famous for providing training datasets for machine learning in numerous fields. These include; Agriculture, Healthcare, Retail, Autonomous Vehicles, Autonomous Flying, and Satelite Imagery.

29Nov

Medical Image Annotation: It’s Role in AI Medical Diagnosis

Artificial Intelligence is becoming widespread with better and more functional computer vision-based AI and Machine Learning models.

With more training data, machine learning algorithms enable AI models to learn more variation hence improving prediction results. This improves the accuracy and applicability in the healthcare sector.

In order to make training data useful and relevant, we use accurately annotated medical images to make body ailments or a disease discernible through machine learning. Medical Image annotation involves preparing such data with a justifiable level of accuracy.

What is Medical Image Annotation?

Medical Image Annotation involves tagging of medical image/data e.g. MRI, CT scan, Ultrasound, etc. for machine learning purposes.

Medical Image Annotation plays a crucial role in the healthcare industry. In this blog, we will cover the important role medical image annotation plays in the modern healthcare sector through machine learning and artificial intelligence. We will also discuss the different types of medical images and how they can be annotated to generate different data sets for specific diseases and ailments.

The role played by Medical Image Annotation in AI Medical Diagnostic.

Medical Image Annotation plays a crucial role in identifying the different types of diseases using AI-powered devices, machines, and computer models.

To all intents and purposes, this process provides actual data to enable machine learning algorithms and models to detect diseases when similar images to the training data are brought before the system.

From simple bone fractures to complicated diseases like cancer, accurate medical image annotation can spot disorders on a microscopic level with precise predictions. Below are a number of ailments that can be diagnosed using Medical Imaging Diagnostics.

Diagnosis of the Brain Disorders

Medical Image Annotation is used to diagnose and identify brain tumors, blood clots, and other neurological diseases. Through a CT Scan or MRI, machine learning models can spot different disorders if properly trained with accurately labeled/annotated images.

Artificial Intelligence in neuroimaging is made possible when brain disorders are well labeled/annotated and fed into machine learning algorithms to enable accurate predictions.

When a model is fully trained and completely adapted to be used in place of a radiologist, it can be able to make better and more accurate Medical Imaging Solutions. This will greatly save time and efforts used by radiologists to make diagnoses.

Liver Problems Diagnosis

Medical experts using medical imaging formats and ultrasounds generally diagnose liver-related issues and complications.

Physicians usually identify, denote, and observe ailments by visually evaluating medical images. Unlike AI and ML models, physicians are prone to biases stemming from their personal experiences.

Detecting Cancer Cells

Using AI-enabled machines to identify different types of cancers plays a big role in early diagnosis hence saving people from life-threatening diseases. When a cancer diagnosis is done late, it takes great effort and time to cure or recover from such an illness.

With the advent of AI models specially trained with accurate medical image annotations, it enables computer models/algorithms to learn from the data hence making correct predictions on the type and stage the cancer is at.

Diagnostic Image Analysis

Diagnostic imaging e.g. MRI, CT Scan, and X-ray scans provide a better visual option to detect diseases and figure out the actual ailment and give the necessary treatment.

Our medical image annotation professionals generate imaging and tag distinct disease symptoms using different annotation methods.

Medical Records Documentation

Medical Image Annotation is also used on a number of medical documents and files through text annotation to make the data discernible to AI and ML models. Medical records data on patients and their health conditions are used to train perception models.

With a professionally managed workforce of experienced annotators, medical records data can be labeled with great accuracy while maintaining the confidentiality of such data.

Below are some of the types of documents that can be annotated using Medical Image Annotation;

  • X-rays
  • CT Scan
  • MRI
  • Ultrasound
  • Medical Records

To build an AI model that makes correct predictions, AI medical diagnostics firms need a wide range of data that’s accurately annotated to train the models.

Impact Outsourcing provides top-of-shelf medical image annotation services at a cost-effective price. With us, your data sets will always be of the highest quality, which is just what is needed to train your perception models.

Whether in the field of healthcare, automotive, agriculture, or autonomous machines, we always got you covered with our world-class data annotation services.

29Nov

Defining Impact Sourcing

Outsourcing has always been a viable option ever since entrepreneurs found alternative ways that are cost-effective as compared to doing everything internally. In addition, offshoring has been a feature in an entrepreneur’s vocabulary over the last two decades.

What about Impact Sourcing? Simply put, this is the new wave. So far, the best definition for impact sourcing has been; Impact Sourcing is exporting/offshoring digital work to areas and workers that would traditionally never access it.

With this definition in mind, it’s fair to say that impact sourcing simply moves labor from 2nd to 3rd tier locations. For most companies, this is typically the case. However, a thorough definition of Impact Sourcing covers how workers and employers confront the relationships between themselves and the offshored work.

A good example of this is Digital Jobs Africa Initiative, under the Rockefeller Foundation. The foundation has in recent times put emphasis, both in thought and effort, on crafting meaningful strategies for Impact Sourcing. As a result of this undertaking, a robust definition of Impact Sourcing has cropped up:

Impact Sourcing is a socially conscious branch of the Business Process Outsourcing (BPO) and Information Technology Outsourcing industry that purposefully employs people with limited opportunity for sustainable employment, mostly in low-income areas/countries.

29Jun

Image Annotation for Computer Vision

Image Annotation For Computer Vision

For any artificial intelligence project to be a success, the images used to train, validate, and test your computer vision algorithm plays a key role. To properly train an AI model to recognize objects and make predictions just as humans do, we thoughtfully and accurately label images in every data set.

The more diverse your image data is, the trickier it gets to have them annotated in line with all your specifications. This can end up being a setback for both the project and its eventual market launch. For these reasons, the steps you take in crafting your image annotation methodologies, tools, and workforce are all the more important.

Image Annotation for Machine Learning

What is image annotation?

In both deep learning and machine learning, image annotation is basically labeling or categorizing images through an annotation tool or text tools to convey data attributes that the AI model is training to recognize. When annotating an image, you’re basically adding metadata to a data set.

Image annotation is a branch of data labeling also known to as tagging, transcribing, or processing. Videos can also be annotated, either frame by frame or as a stream.

Types of images used for machine learning

Machine learning involves annotation of both images and multi-frame images e.g. videos. As earlier indicated, videos can either be annotated frame by frame or as a stream.

There are usually two data types used in image annotation. They are;

  1. 2-D images and video
  2. 3-D images and video

How to Annotate Images

Images are annotated using image annotation tools. These tools are either are available in the open market, freeware, or open-source. Depending on the volume of data on hand, the need for an experienced workforce to annotate data comes to play. Data annotation tools come with a set of capabilities that a workforce can utilize to annotate images, multi-frame images, or video.

Methods of image annotation

There exist four methods of image annotation for training your computer vision or AI model.

  1. Image Classification
  2. Object detection
  3. Segmentation
  4. Boundary Recognition

Image Classification

Image classification is a branch of image annotation that works by identifying the presence of similar objects in images across a dataset. Assembling images for image classification is also known as tagging.

Object Recognition/Detection

Object recognition works by identifying the presence, location, and number of either one or more objects in an image and accurately labeling them.

Depending on compatibility, we use different techniques to label objects within an image. These techniques include bounding boxes and polygons.

Segmentation

Segmentation annotation is the most complex application of image annotation. We use Segmentation annotation in a number of ways to examine visible content in images and decide if objects within the same image match or differ. There are three types of segmentation:

  1. Semantic Segmentation
  2. Instance Segmentation
  3. Panoptic Segmentation

Boundary Recognition

We can train machines to identify lines/boundaries of objects within an image. Boundaries can consist of edges from a particular object, topography areas shown in the image, or any man-made boundaries that appear on the image.

When accurately annotated, we use images to teach an AI model on how to see akin designs in unlabeled images.

We use boundary recognition to teaching AI models on how to identify international boundaries, pavements, or even traffic lines. For the eventual safe use of autonomous vehicles, boundary annotation will play a very key role to make it all possible.

How to do Image Annotation

In order to make annotations in your image data, you need a data annotation tool. And with the advent of AI, data annotation tools use cases have propped up all over the globe.

Depending on your project needs and the resources at your disposal, you can tailor-make your own annotation tool. If you take this path, you will need resources and experts to continuously maintain, update, and improve the tool over time.

Image Annotation Methods

Depending on your annotation tool’s feature sets, image annotation comprises the following techniques:

  1. Bounding Box
  2. Landmarking
  3. Polygon
  4. Tracking
  5. Transcription

Bounding Box

This technique world by drawing a box around the object in focus. This method works well for relatively asymmetrical objects e.g. road signs, pedestrians, cars, etc. We also use bounding boxes when we have less interest in an object’s shape and when there are no strict rules on occlusion.

Landmarking

Landmarking works by plotting characteristics in data. We mainly use it in facial recognition technology to detect emotions, expressions, and facial features.

Polygon

The polygonal annotation works by marking each of the highest points (vertices) on the target object and annotating its edges. For this reason, we use polygonal annotation when the object is of a more irregular shape e.g. houses, land, vegetation, etc.

Tracking

We apply tracking to tag and plot an object’s motion through several frames in a video.

A number of annotation tools have interpolation attributes that permit annotators to tag each frame at a time.

Transcription

We apply transcription when annotating text in an image or video. Annotators use this when there is different information (i.e. image and text) in the data.

How Organizations are Doing Annotations

Companies employ a blend of software, processes, and people to collect, clean, and annotate images. Generally, organizations have four options when selecting an image annotation workforce. The quality of work is dependent on how well the team is managed and how their KPIs are set.

Employees

This involves having people on your payroll, either part-time or full time. This allows you to mold in house expertise and be adaptable to change. However, scaling up when using an internal team may prove to be a challenge. This is because you take full responsibility and expenses in hiring, managing, and training workers.

Contractors

Contractors are freelance workers who train to do the work. With contractors, there is some flexibility in the event that you want to scale up. However, just like employees, you will take responsibility for managing the team.

Crowdsourcing

Crowdsourcing is an anonymous, make-do source of labor. It works by using third party platforms to reach large numbers of workers. Subsequently, the users on the platform volunteer to do the work described to them. With crowdsourcing, there is no guarantee for landing annotation experience and you are constantly in the dark with regard to who is working on your data. As a result, the quality will be low since you cannot vet crowdsourced workers in the same way as in-house employees, contractors or managed teams are.

Managed Teams

Managed teams are basically the outsourcing route. A managed team applies professionalism in both training and management. It works by you sharing your project specifications and annotation process. In return, the managed teams aid in scaling up when the need arises. As the team continues working, their domain knowledge with your use case is likely to improve with time.

Advantages of Outsourcing to Managed Teams

  1. Training and Context

To get high-quality data for machine learning, basic domain knowledge, and understanding of image annotation is a must. A managed team guarantees high quality labeled data. This is because you can teach them context, relevance, and setting of your data. Consequently, this guarantees that their knowledge only increases over time. Unlike crowdsourcing, managed teams have staying power and are able to retain domain knowledge.

  1. Agility

Machine learning being an iterative process, you may need to alter project rules and workflow as you validate and test your AI model. With a managed team, your ensured flexibility to integrate changes in data volume, task duration, and task complexity.

  1. Communication

With a managed image annotation team, you can create a closed technological feedback loop. This ensures seamless communication and cooperation between your internal team and annotators. In this way, workers are able to share insights on what they noticed when working on your data. With their insights, you can opt to adjust your approach.

 

18Jun

Video Annotation in Machine Learning and AI

Video annotation, like image annotation, aids in the recognition of objects by modern machines using computer vision. Detecting moving things or objects in videos and making them identifiable using frame-to-frame. For example, a 60-second video clip with a 30 fps (frames per second) frame rate, has 1800 video frames, which may be treated as 1800 static images. Videos are often treated as data for enabling technological applications to perform real-time analysis for producing accurate results. Video annotated data is required to train AI models designed with deep learning is the significant goal of video annotation. The most frequent uses of video annotation typically include autonomous cars, tracking human activity and posture points for sports analytics, and face expression identification, among others.

In this blog, we will understand about video annotations, how it works, features that make annotating frames easier, uses of video annotations and the best video annotation labeling platform to choose.

What is Video Annotation?

The process of analyzing, marking or tagging and labeling video data is called video annotation. The practice of correctly identifying or labeling video footage is known as video annotation. It is performed in order to prepare it as a dataset for machine learning (ML) and deep learning (DL) models to be trained on. In simple terms, human annotators examine the video and tag or label the data as per predefined categories to compile training data for machine learning models.

How Video Annotation Works

Annotators use multiple tools and approaches in video annotation that are essential to do annotation. The video annotation procedure is lengthy often due to the requirement of annotation. A video can have up to 60 frames per second, which implies that annotating video takes much longer time than annotating images and necessitates the use of more complex or advanced data annotation tools. There are multiple ways to annotate videos.

Also Read: Why Data Annotation is Important for Machine Learning and AI?

1. Single Frame: In this method, the annotator divides the video into thousands of pictures, and then performs annotations one by one. Annotators can sometimes accomplish the task with the use of a copy annotation frame-to-frame capability. This procedure is quite time-consuming. However, in other instances, when the movement of objects in the frames under consideration is less dynamic, this may be a preferable alternative.

2. Streaming Video: In this method, the annotator analyzes a stream of video frames using specific features of the data annotation tool. This method is more viable and allows the annotator to mark things as they move in and out of the frame, allowing machines to learn more effectively. As the data annotation tool market expands and vendors extend the capabilities of their tooling platforms, this process becomes more accurate and frequent.

Types of Video Annotations

There are different annotation methods. The most commonly used methods are 2D bounding boxes3D cuboidslandmarkspolylines, and polygons.

  • 2D Bounding Boxes: In this method, we use rectangular boxes for object identification, labeling, and categorization. These boxes are manually drawn around objects of interest in motion across several frames. For an accurate depiction of the item and its movement in each frame, the box should be as close to every edge of the object as feasible and labeled appropriately for classes and characteristics.
  • 3D Bounding Boxes: For a more realistic 3D depiction of an item and how it interacts with its environment, the 3D bounding box method is used as it indicates the length, breadth, and estimated depth of an object in motion. This method is most efficient for detecting common to specific classes of objects.
  • Polygons: When 2D or 3D bounding boxes are insufficient to correctly depict an object in motion or its form, Polygon method is frequently employed. It typically necessitates the labeler’s high level of accuracy. Annotators must create lines by placing dots around the outer border of the item they want to annotate with precision.
  • Landmark or Key-point: By generating dots throughout the image and linking these dots to build a skeleton of the item of interest across each frame, key-point and landmark annotation are widely used to identify tiniest of objects, postures and shapes.
  • Lines and Splines: While lines and splines are most commonly utilized to teach robots to recognize lanes and borders, notably in the autonomous driving sector. The annotators simply draw lines between locations that the AI program must recognize across frames.

Use of Video Annotations

Apart from identifying and recognizing objects, which can also be done using image annotation, video annotation is used in building the training data set for visual perception-based AI models. For computer vision object localization, localizing the objects in the video represents another use of video annotation. In reality, a video has numerous objects, and localization aids in discovering the primary item in the image, which is the thing that is most apparent and concentrated in the frame. Object localization’s primary goal is to anticipate the object in an image and its bounds.

Another important goal of video annotation is to train the computer vision-based, AI, or machine learning models to follow human movements and predict postures. This is most commonly used in sports fields to track athletes’ activities during contests and sporting events, allowing robots and automated machines to learn human postures. Another application of video annotation is to capture the item of interest frame by frame and make it machine-readable. The moving items appear on the screen and are tagged with a specific tool for exact recognition utilizing machine learning techniques to train AI models based on visual perception.

10Jun

Image Annotation for Machine Learning?

Training of drones, autonomous vehicles, and other computer-vision based models needs annotated images and videos so that the machines can identify and interpret the object without much human intervention. The data which is fed in these machine algorithms to understand images and videos, text or audio created the need for the annotations.

Majorly, image and video annotation are widely used. However, the process of annotating is almost the same but video annotation needs more precision and accuracy and it is a bit difficult because of the movement of the target object i.e. the target object continuously moves in a video so it is slightly difficult to annotate the videos as it needs specialization and experience.

Image Annotation

Image annotation is one of the basic tasks to train the machines or computers to interpret and identify the visual world. Images annotated by the annotators are used to train machine learning algorithms which helps them to identify the objects that are given in the image. This gives computers the ability to see and identify the things as humans do.

Image annotation means selecting the given objects in the image and labelling them by their names. It helps machines to recognize things/objects so that they can make correct decision without any human intervention. For example, if a cat needs to be annotated then, that cat in the image will be marked and labelled as a cat and this data is fed into an algorithm to train the machine so that next time the machines can automatically recognize the object.

Pixel accurate image annotations

Based on algorithms there are several types of annotations. Few are:

  • Bounding box annotation
  • Polygon annotation
  • Semantic annotation
  • Key point annotation
  • 3D point cloud annotation
  • Landmark annotation

The most commonly used image annotation is the bounding box in which rectangle boxed are placed or marked around the target object. However, this has some major issues:

1. One needs a huge number of bounding boxes to reach over 95% detection accuracies.

2. This technique does not allow perfect detection regardless of how much data you use.

3. The detection becomes extremely complicated for obstructive objects.

The future

All these issues which are mentioned above can be solved with a pixel-accurate annotation. For example, pixel level accuracy is of utmost importance is the medical field where machine learning models requires high level of precision and accuracy for the model to make sound judgment and deliver accurate results. Machine Learning Projects in Medical space are highly sensitive and depends significantly upon accuracy of the data being fed into them. Even minor inaccuracies in the medical machine learning data could be detrimental for the entire operations and could lead to disastrous results. Hence, this is where pixel-accurate annotations plays a huge part in keeping it together. And a lot of it depends upon the quality of the images and datasets.

Yet, the most commonly used tools are majorly dependent on point-by-point object selection, which is time-consuming and costly too. Pixel-accurate annotations have a huge advantage to aerial imagery as well. However, the tools for such annotations depend on the slow point-by-point annotation. As a result the time taken to complete the task is way too much and the results are also sensitive to human errors. To train an algorithm to identify the roof types in the satellite images, annotator needs to annotate thousands to millions of images of roofs in different cities, weather conditions, etc and when the image is not accurate and gets there timely then the technology and the output will suffer because the quality of image plays a crucial role in the annotation.

However, there are researches that have helped in reducing the impact of image quality. Addressing this problem, the research community have made efforts towards creating more efficient pixel-accurate annotation methods. The community is developing many exciting pre-processing algorithms that we can use to improve image quality and ensure better quality segmentation.

A company whose competitive advantage depends on accurate image annotation can reach Analytics as we are delivering best-in-class image annotation services with several others. The professionals in analytics have several years of technical experience in using machine learning and artificial intelligence technologies to develop projects in healthcare, retail, autonomous flying, self-driving, agriculture, robotics and among others. Here one will get the utmost satisfaction to meet your requirements at affordable pricing.