Knowledge&Technology

Auto Image Tagging: Explained

In this article, we will discuss what auto image tagging is, the difference between manual and auto image tagging, how it works, and the benefits of image tagging.
Aleyna Güner
5 minutes

Auto Image Tagging: Explained

Storage capacities are increasing as the Social Web and digital cameras grow in popularity, with hundreds of photos shared through these applications. The majority of Social Web applications allow users to describe their photos by tagging them. However, because tagging is voluntary, most of these photos were left untagged or with insufficient tags. As a result, searching for and retrieving these photos is difficult. 

The process of labeling images with keywords to make them more searchable is known as image tagging. While manual photo tagging is possible, image tagging technology automates the entire tagging process across image libraries of any size, making the process faster and more efficient.

In this article, we will examine the following subjects:

- What Auto Image Tagging is

- Manual tagging versus AI image tagging

- How Auto Image Tagging Works?

- Benefits of Auto Image Tagging 

- How to Improve Auto Tagging with Custom Training with Plugger.ai

Before we dig deeper, if you are looking for an automatic image tagging solution, you can click HERE to try Plugger.ai Automatic Image Tagging for free.

Let's find out what Automatic image tagging is.

What is Auto Image Tagging?

Businesses must deal with large volumes of visual content daily, from stock photo libraries to advertising, travel, and booking platforms. Some of these businesses are also interested in user-generated visual content, which may need to be tagged and categorized.

In the visual labeling process, using an automatic image tagging solution instead of manually saves businesses time and effort, making the process more manageable. It allows efficient and intuitive search and discovery of relevant images from large libraries based on pre-assigned tags.

In essence, image tagging means setting keywords for elements contained in an image. For example, imagine you're looking at a wedding photo, most likely with tags like "wedding," "couple," and "marriage." On the other hand, depending on the system, it may also have labels such as colors, objects, and other specific elements and properties contained in the image - this includes abstract terms like "love," "relationship," and more.

After assigning keywords to images, users and businesses can enter the keywords related to what they're looking for in a search field to find the images they need. Based on the wedding photo example, they can enter the keyword 'couple' or 'love,' and the results will be sorted by the most relevant.

Knowing the difference between image tagging and Metadata is essential. Metadata usually includes technical data about an image, such as height, width, resolution, and parameters, and Metadata is automatically embedded in image files. On the other hand, image tagging involves defining what is visible in an image with keywords.

image tagging

Manual tagging versus AI image tagging

As previously stated, you could manually tag the images, in which a person examines each picture one by one and adds tags to it in your digital asset management system. Although this works great for a small number of images, it is not scalable when dealing with an extensive image repository. Manually tagging images will take a significant amount of time and effort. 

The second and more important concern is maintaining consistency and avoiding tagging errors. Each person might tag an image based on their vocabulary or interpretation of the image. 

Imagine an image of a red car, the person tagging it might put tags like "car," "red car," or "luxury car." All of these are correct, but they might not be under business requirements.

As a result, much effort would be expended in training a large team to maintain a consistent tagging vocabulary as a business. Manual spelling errors could also occur, effectively rendering tagging useless because you would be unable to search for an image with that tag.

Artificial Intelligence provides a much better alternative, with advancements in Computer Vision allowing computer software to see and interpret an image as humans do, identify objects in it, and label them automatically.

AI image tagging is done almost in milliseconds and can be done for thousands of images simultaneously, thanks to computer programs, making it exponentially faster than manual tagging.

Furthermore, the same Ai system would always generate the same tags for the same image, eliminating human inconsistency in image tagging. You save your hours by not having to hire and train a huge workforce, and the ability to tag images with AI in real-time consistently contributes to better business workflows.

Moreover, there is no need for software to do this anymore because, with the Plugger.ai no-code platform, you can create your own ai based image tagging application from scratch in seconds.

Now that we have covered what image tagging is and the difference between manual tagging and AI image tagging, let's take a look at how it works

How Auto Image Tagging Works?

Image tagging entails identifying people, objects, places, emotions, abstract concepts, and other attributes that may be associated with a visual. They are then assigned to the visual using predefined tags.

Once people search within an image library, users could thus enter keywords and receive results based on them. This is how people can easily access visuals containing the required elements.

With cutting-edge technology, auto-image tagging has evolved into a complex process with sophisticated consequences. It not only describes the actual objects, colors, and shapes found in an image, but also several other properties. To give an example of this subject, image tagging can encompass the general atmosphere depicted in an image, concepts, emotions, relationships, and much more.

Image tagging's high level of complexity today allows for more robust image discovery options. Search capabilities improve and become more precise when descriptive tags are attached to visuals. This means that people can actually find the images they're looking for. 

Benefits of Auto Image Tagging 

There are several advantages to using AI-based image tagging tools. The photo tagging system makes it easier for people to find images quickly based on search terms and keeps folders and libraries easily accessible. Image tagging makes the process quick and easy if a customer searches your website or a team member searches your database for an image.

auto image tagging technology

1. You will save time on administrative tasks.

An AI image tagging solution automatically tags images based on the objects that appear in them. Its artificial intelligence-powered technology seeks relevant keywords and keeps adding them as tags to every image you import into a content library or webpage. This means that images are tagged more accurately, and team members save time on manual image tagging.

Following the completion of the instant auto-tagging process, anyone with access to your media library can search, edit, and update tags as needed. The AI learns alongside you as you make changes, ensuring that each tagging round is better than the last.

2. Simplify asset management

As previously stated, the same auto-tagging technology employs artificial intelligence to tag images as they are imported. Image tagging solution streamlines asset management by making it simple to categorize and search images based on specific criteria. Less effort for your team and less time spent compensating for misplaced assets means more time to focus on higher ROI tasks.

3. Easily find what you need when you need it

With the AI-based automatic image tagging system, you can easily find the image you are looking for at any time.

Because natural language processing (NLP) technology powers image tagging solutions, it can accurately understand the true intent of your image search and suggest relevant content accordingly. This brings all options to the table, allowing you to select the best one, regardless of whether it was the original asset you intended to use.

How to Improve Auto Tagging with Custom Training with Plugger.ai

The best thing about Artificial Intelligence-based automatic labeling is that it can improve over time. Thanks to the deep learning model, it can be trained with additional data to recognize special elements and provide accurate labeling in certain industries.

Thanks to Plugger.ai data training, your automated tagging system can quickly learn to identify unique items specific to your business niche and with examples. It saves you the trouble of manual labeling. You can create your own image tagging application from scratch in minutes without writing a single line of code according to the needs of your own company and industry.

You can fully customize the procedure to the unique requirements of your operations and utilize the power of deep learning models by having your auto-tagging platform undergo custom training. It's especially beneficial for companies in specialized industries or with other unique tagging requirements.

You can use the Plugger.ai auto image tagging system from the web or your own edge devices.

Final Words

AI advances now enable us to use human vision-like capabilities in our business.

Using this technology in conjunction with Plugger.ai solutions to tag our images with the image's actual content can greatly simplify asset management and search.

You can begin using AI image tagging for image management by searching for a completely free trial with Plugger.ai and its integration of AI tagging results with Slack, Google Drive, and other services.

Sign up now and start providing great image experiences on your website!

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