29Jun

Image Annotation for Computer Vision

adminEntrepreneurs 3

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

adminConsultation

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 boxes, 3D cuboids, landmarks, polylines, 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?

adminLeadership

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.