5 Ways video annotation is transforming machine learning

5 Ways video annotation is transforming machine learning 1
Machine learning

Machine learning is revolutionizing the world. More and more gadgets, applications, and software utilize this new technology to provide the best experience possible.


Disclosure: This is a guest post and the author’s views here do not necessarily reflect those of the blog owner.

The idea of video annotation, when it first appeared a few years back, was that humans would watch videos and interesting tag segments. Machines would then use this data to apply the most appropriate algorithms. Now we know better. Well, sort of.

See also how to watch YouTube video in VLC media player.

Here are the 5 ways video annotation is transforming machine learning:


5 Ways video annotation is transforming machine learning

1. Makes machine learning more accurate

Video annotation enhances data quality by providing a useful starting point for Machine Learning algorithms when building a model or allowing an underlying model to be trained on higher quality data. For example, videos annotated with high-quality captions enable algorithms to be trained on more accurate information, which reduces error rates in classification tasks such as voice recognition or object detection.

One of the challenges of machine learning is figuring out what data sets are relevant and which ones are not. With video annotation tools, you can get your hands on images, text bubbles, and other information that can quickly help the algorithm get the gist of what is being said in a video. It enables it to learn more efficiently and with greater accuracy.

2. Automatic segmentation of portions

One aspect of annotating videos is the segmentation task, where you mark up the objects and parts of the scene that you want the algorithm to focus on. Video annotation tools can do this automatically, so you don’t have to define every single frame manually. For example, you can mark different types of clothing, cars, or even people in a crowd so that your network can learn how to identify them accurately later on.

In addition to segmenting your video, video annotation tools also allow you to provide text descriptions for each segment to give your network more information about what it should be looking for. It makes your training data much more detailed than labeling each segment by hand.

3. The amount of information being collected will increase

Video annotating plays a crucial role in its success as it collects and processes massive amounts of data. Deep Learning, a branch of machine learning that focuses on building a computer program that allows computers to learn by crunching data, is responsible for many technologies we use every day.

Deep learning works based on the concept known as “data ingestion,” which means that a computer will learn from examples and then be shown something new and compare its theory with the actual picture or video that’s being offered. This process is called “training.”

Deep learning needs massive amounts of data to train itself, and video annotation providers are helping to provide such data. The amount of information being collected will increase due to video annotations, thus allowing deep learning to reach more accurate results.

Data collection used to be the biggest challenge for companies interested in implementing machine learning. However, the introduction of video annotation tools has allowed companies to collect more data at a much faster rate. The ability to classify videos has become easier with the availability of these tools, which will enable users to annotate videos with tags and labels easily.

4. Helps the algorithms learn faster

The key to making machine learning more accurate is to train it on multiple examples of correct answers. The more models your algorithm has access to, the faster it can learn how to do its job well.

Annotating videos is a popular, valuable, and challenging way of helping algorithms learn faster. The process, which involves labeling data within a video with notes, tags, or other information, allows the algorithm recognizes changes, objects, and events in the video.

The main benefit of video annotating is that it allows users to provide feedback on the machine learning model. The human annotator can help the algorithm learn from its mistakes and improve over time.

5. Automated facial recognition algorithms

Facial recognition and identification software has been around for some time but has never been able to perform at a level that is useful for applications outside of security. It is all set to change thanks to AI, which can complete facial recognition tasks in seconds as opposed to hours and days with traditional methods.

To build facial recognition software that can recognize people that we have never met before, we need to train it with enough data from the video annotation tools to make accurate predictions. After all, we don’t expect our automated system to tell the difference between an unknown face and the neighbor’s face unless it sees images of both beforehand.


Final Words

While the primary focus here is on the benefits of annotating videos in the context of machine learning, there are also plenty of opportunities for this type of technology to be used in other industries. Whether you’re working in business intelligence or a law firm utilizing these technologies, there are plenty of ways that video annotating can improve your services.

This is a guest post by Samantha Higgins.

Samantha Higgins is a professional writer with a passion for research, observation, and innovation. She is nurturing a growing family of twin boys in Portland, Oregon with her husband. She loves kayaking and reading creative non-fiction.


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