What is image tagging for SEO?

In this post, we'll explain everything you need to know about optimizing images for SEO via image tagging.
Published
May 24, 2022
Author

Kai Blum

Category

Strategy

Image tagging is the process of identifying and labeling the contents of an image. This can be done manually by someone looking at the image and typing in a description, or done automatically using machine learning technology.

When images are tagged, this information becomes metadata that can be used for a variety of purposes such as web accessibility, content management, and SEO. Below, we'll explain everything you need to know about optimizing images for SEO via image tagging.

How does image tagging work?

Manual image tagging involves looking at an image and typing in a description of what you see. This can be done by one person or by a team of people.

Automatic image tagging uses machine learning technology to identify the contents of an image. Tagging system technology is trained on a set of images that have been labeled with descriptive tags. When it sees an image, it will identify the objects and label them accordingly.

One of the most advanced tools on the market for automatic image tagging, or auto-tagging, is Amazon AWS Rekognition, a machine learning service that can identify the contents of images and videos. It also provides highly accurate facial analysis and facial recognition.

When images are tagged, this information becomes metadata that can be used for a variety of purposes such as web accessibility, content management, and SEO.

Pros and cons of image tagging

There are a number of benefits and drawbacks to image tagging:

Pros

Cons

It can improve your SEO. When you tag your images with descriptive keywords, it helps Google and other search engines understand what the image is about. This can help your content rank slightly higher and enable your images to appear in image search results.

The image-tagging process can be time-consuming and tedious. Manual image tagging requires someone to look at each image and type in a description. Depending on the number of images, this can be hard to scale.

It can make your content more accessible. Tagging images with descriptive labels can make them easier for people who are visually impaired to understand.

It can be inaccurate. Automatic image tagging is not perfect and can sometimes identify the wrong objects in an image or mislabel them.

It can help improve digital asset management and streamline workflows. By tagging your images, you create a database of image metadata that can be used for a variety of purposes, such as categorization.

Image recognition tools can be expensive. Amazon AWS Rekognition is a paid service, as are similar tools.

Image tagging vs. metadata

When discussing image management, it's important to distinguish image tagging from metadata.

Metadata is data that is attached to a piece of content to provide additional information about it. For example, you can add metadata to an image file to track who created it, when it was created, and where it was created. Metadata can also be used to define image file formats.

Image tagging is the process of identifying and labeling the contents of an image. 

Therefore, the main difference between image tagging and metadata is that image tagging describes what the image actually looks like and the metadata contains information about the image itself.

However, image tagging can also be used to create metadata. When you tag your images with descriptive keywords, you are creating a database of image metadata that can be used for a variety of purposes, such as image alt tags, which is the text that appears if your image fails to properly load.

How much do image alt tags help SEO?

Keyword-optimized image alt tags are not a game changer and will not magically improve rankings, but they are simply one of several best practices to optimize a page for a specific keyword. And together with other optimized page elements, they tell Google what the page is about.

Alt tags are still worth optimizing, though, because they could become a tiebreaker when Google has to choose between two pages of similar value. This cannot be underestimated, since every time a page moves up one position on page 1 for its primary keyword, organic traffic for that keyword usually doubles. Since SEO is extremely competitive, you should not leave any optimization opportunity untouched.

But before you invest time optimizing your image alt tags for SEO, you should ask yourself if your content is really the best content online targeting your primary keyword. If not, improving that content will have a significantly higher impact on rankings than image tagging.

It should also be noted that Google takes the content around an image into account when selecting it for image search results, i.e. the topic of the article as well as keyword usage in the meta title tag, the on-page headline and text.

If the content of the alt tag is consistent with the other page elements, then chances for good rankings in image search results are good. Likewise, the content of the alt tag, as well as the use of keywords in the image file name, are another signal to Google what the page is about.

For example, this image is manually tagged with the keyword phrase "green grocery basket." (Ideally, the image file name should also include this keyword phrase, e.g. green-grocery-basket.png.)

Examples of image tags for SEO

Here are a few examples of image tagging in action:

  • Above is a .png image of a green grocery basket. The image is manually tagged with the keyword phrase "green grocery basket." (Ideally, the image file name should also include this keyword phrase, e.g. green-grocery-basket.png.)

  • An image of a building on fire, which we won't include here but I'm sure you can imagine. The image is tagged by software with the keywords "building, fire."

Our guidance is that manual tagging is ideal for SEO, since a tagging software will not know which keyword phrase you’re targeting with your page. But as we mentioned above, this is not always doable at scale.

Using AI for image tagging

While image tagging can be done manually, it's often more efficient to use machine learning technology for this task.

Machine learning is a type of artificial intelligence that allows computers to learn from data and make predictions about future events.

There are a number of advantages to using AI for image tagging:

  • Automatic image tagging is not perfect, but for SEO it is better than no tagging at all.

  • It can expedite and streamline the tagging process, since machines can process images much faster than humans can.

It can scale to large numbers of images. Machine learning algorithms can handle large volumes of data and tag them more and more accurately over time.

How to tag an image manually for SEO

To manually tag an image for maximum SEO benefits, you need to choose a keyword phrase that accurately describes it and is close to matching the primary keyword phrase of the page. If you have several images, you can use keyword variations.

You can then add these keyword phrases to the image metadata. This can be done in a number of ways, depending on what platform you are using:

  • HTML tags: You can use HTML tags to add keywords to your images. The most common way to do this is by adding an “alt” attribute to the image tag. 

  • Content management systems: Most content management systems allow you to also add annotations to your images in the form of metadata. This metadata can include keywords and other information such as the image dimensions, file name, and file type.

Fortunately, Contentful’s flexible, extensible platform helps make image tagging a breeze. Learn more about how to optimize images with Contentful or start building for free today. 

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