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Free Google AI Image Analysis Tool

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Free Google AI Image Analysis Tool

Google offers an AI image classification tool that analyzes images to classify the content and assign labels to them.

The tool is intended as a demonstration of Google Vision, which can scale image classification on an automated basis but can be used as a standalone tool to see how an image detection algorithm views your images and what they’re relevant for.

Even if you don’t use the Google Vision API to scale image detection and classification, the tool provides an interesting view into what Google’s image-related algorithms are capable of, which makes it interesting to upload images to see how Google’s Vision algorithm classifies them.

This tool demonstrates Google’s AI and Machine Learning algorithms for understanding images.

It’s a part of Google’s Cloud Vision API suite that offers vision machine learning models for apps and websites.

Does Cloud Vision Tool Reflect Google’s Algorithm?

This is just a machine learning model and not a ranking algorithm.

So, it is unrealistic to use this tool and expect it to reflect something about Google’s image ranking algorithm.

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However, it is a great tool for understanding how Google’s AI and Machine Learning algorithms can understand images, and it will offer an educational insight into how advanced today’s vision-related algorithms are.

The information provided by this tool can be used to understand how a machine might understand what an image is about and possibly provide an idea of how accurately that image fits the overall topic of a webpage.

Why Is An Image Classification Tool Useful?

Images can play an important role in search visibility and CTR from the various ways that webpage content is surfaced across Google.

Potential site visitors who are researching a topic use images to navigate to the right content.

Thus, using attractive images that are relevant for search queries can, within certain contexts, be helpful for quickly communicating that a webpage is relevant to what a person is searching for.

The Google Vision tool provides a way to understand how an algorithm may view and classify an image in terms of what is in the image.

Google’s guidelines for image SEO recommend:

“High-quality photos appeal to users more than blurry, unclear images. Also, sharp images are more appealing to users in the result thumbnail and increase the likelihood of getting traffic from users.”

If the Vision tool is having trouble identifying what the image is about, then that may be a signal that potential site visitors may also be having the same issues and deciding to not visit the site.

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What Is The Google Image Tool?

The tool is a way to demo Google’s Cloud Vision API.

The Cloud Vision API is a service that lets apps and websites connect to the machine learning tool, providing image analysis services that can be scaled.

The standalone tool itself allows you to upload an image, and it tells you how Google’s machine learning algorithm interprets it.

Google’s Cloud Vision page describes how the service can be used like this:

“Cloud Vision allows developers to easily integrate vision detection features within applications, including image labeling, face and landmark detection, optical character recognition (OCR), and tagging of explicit content.”

These are five ways Google’s image analysis tools classify uploaded images:

  1. Faces.
  2. Objects.
  3. Labels.
  4. Properties.
  5. Safe Search.

Faces

The “faces” tab provides an analysis of the emotion expressed by the image.

The accuracy of this result is fairly accurate.

The below image is a person described as confused, but that’s not really an emotion.

The AI describes the emotion expressed in the face as surprised, with a 96% confidence score.

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Composite image created by author, July 2022; images sourced from Google Cloud Vision API and Shutterstock/Cast Of Thousands

Objects

The “objects” tab shows what objects are in the image, like glasses, person, etc.

The tool accurately identifies horses and people.

Screenshot of Google Vision toolComposite image created by author, July 2022; images sourced from Google Cloud Vision API and Shutterstock/Lukas Gojda

Labels

The “labels” tab shows details about the image that Google recognizes, like ears and mouth but also conceptual aspects like portrait and photography.

This is particularly interesting because it shows how deeply Google’s image AI can understand what is in an image.

Screenshot of Google Vision AI identifying objects within an uploaded photoComposite image created by author, July 2022; images sourced from Google Cloud Vision API and Shutterstock/Lukas Gojda

Does Google use that as part of the ranking algorithm? That’s something that is not known.

Properties

Properties are the colors used in the image.

Screenshot of Google Vision tool identifying the dominant colors in an imageScreenshot from Google Cloud Vision API, July 2022

On the surface, the point of this tool isn’t obvious and may seem like it is somewhat without utility.

But in reality, the colors of an image can be very important, particularly for a featured image.

Images that contain a very wide range of colors can be an indication of a poorly-chosen image with a bloated size, which is something to look out for.

Another useful insight about images and color is that images with a darker color range tend to result in larger image files.

In terms of SEO, the Property section may be useful for identifying images across an entire website that can be swapped out for ones that are less bloated in size.

Also, color ranges for featured images that are muted or even grayscale might be something to look out for because featured images that lack vivid colors tend to not pop out on social media, Google Discover, and Google News.

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For example,  featured images that are vivid can be easily scanned and possibly receive a higher click-through rate (CTR) when shown in the search results or in Google Discover, since they call out to the eye better than images that are muted and fade into the background.

There are many variables that can affect the CTR performance of images, but this provides a way to scale up the process of auditing the images of an entire website.

eBay conducted a study of product images and CTR and discovered that images with lighter background colors tended to have a higher CTR.

The eBay researchers noted:

“In this paper, we find that the product image features can have an impact on user search behavior.

We find that some image features have correlation with CTR in a product search engine and that that these features can help in modeling click through rate for shopping search applications.

This study can provide sellers with an incentive to submit better images for products that they sell.”

Anecdotally, the use of vivid colors for featured images might be helpful for increasing the CTR for sites that depend on traffic from Google Discover and Google News.

Obviously, there are many factors that impact the CTR from Google Discover and Google News. But an image that stands out from the others may be helpful.

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So for that reason, using the Vision tool to understand the colors used can be helpful for a scaled audit of images.

Safe Search

Safe Search shows how the image ranks for unsafe content. The descriptions of potentially unsafe images are as follows:

  • Adult.
  • Spoof.
  • Medical.
  • Violence.
  • Racy.

Google search has filters that evaluate a webpage for unsafe or inappropriate content.

So for that reason, the Safe Search section of the tool is very important because, if an image unintentionally triggers a safe search filter, then the webpage may fail to rank for potential site visitors who are looking for the content on the webpage.

Google Vision Safe Search AnalysisScreenshot from Google Cloud Vision API, July 2022

The above screenshot shows the evaluation of a photo of racehorses on a race track. The tool accurately identifies that there is no medical or adult content in the image.

Text: Optical Character Recognition (OCR)

Google Vision has a remarkable ability to read text that is in a photograph.

The Vision tool is able to accurately read the text in the below image:

Screenshot of Vision tool accurately reading text in an imageComposite image created by author, July 2022; images sourced from Google Cloud Vision API and Shutterstock/Melissa King

As can be seen above, Google does have the ability (through Optical Character Recognition, a.k.a. OCR), to read words in images.

However, that’s not an indication that Google uses OCR for search ranking purposes.

The fact is that Google recommends the use of words around images to help it understand what an image is about and it may be the case that even for images with text within them, Google still depends on the words surrounding the image to understand what the image is about and relevant for.

Google’s guidelines on image SEO repeatedly stress using words to provide context for images.

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“By adding more context around images, results can become much more useful, which can lead to higher quality traffic to your site.

…Whenever possible, place images near relevant text.

…Google extracts information about the subject matter of the image from the content of the page…

…Google uses alt text along with computer vision algorithms and the contents of the page to understand the subject matter of the image.”

It’s very clear from Google’s documentation that Google depends on the context of the text around images for understanding what the image is about.

Takeaway

Google’s Vision AI tool offers a way to test drive Google’s Vision AI so that a publisher can connect to it via an API and use it to scale image classification and extract data for use within the site.

But, it also provides an insight into how far algorithms for image labeling, annotation, and optical character recognition have come along.

Upload an image here to see how it is classified, and if a machine sees it the same way that you do.

More Resources:

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Featured image by Maksim Shmeljov/Shutterstock



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SEO

Link relevancy trumps volume for SEO

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Data speaks Link relevancy trumps volume for SEO

30-second summary:

  • Earned media coverage is more valuable than ever for your website
  • Digital PR is just as important as technical SEO
  • A large volume of links is the goal, what’s stopping someone from picking the most newsworthy idea, even if it has nothing to do with your client?

In 2022, it’s impossible to deny the benefit that digital PR as a tactic has on an organic growth strategy. Earned media coverage is more valuable than ever for your website. You could be doing everything right for SEO, but if you’re not building links, you’re still missing out on the increased search visibility, organic traffic, and brand awareness that backlinks bring to your business.

Last year, Google’s John Mueller finally weighed in on digital PR as a tactic and confirmed what we’ve all known for a while now: that it’s just as important — if not more — as technical SEO.

As digital PR is still a relatively “young industry” that’s only just sprouted up in the past 10 years, many PR pros have relied on “viral” campaigns to boost the backlink portfolio of their clients. These viral campaigns are often celebrated but are often created with little regard to how relevant, or “on-brand” those ideas really are.

After all, if a large volume of links is the goal, what’s stopping someone from picking the most newsworthy idea, even if it has nothing to do with your client?

In 2022, link volume is no longer the goal (or shouldn’t be)

While many PR pros’ were evaluating their success around this one key metric (link volume) others in the industry have suspected for a while now that the relevance of linking coverage is a key factor Google looks at when assigning “value” to links.

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Once again, John Mueller has settled the debate about link volume vs link relevance,  coming out in 2021 and saying that ‘the total number of links’ doesn’t matter at all.

This clarity has helped refocus the digital PR industry and forced PR pros to re-evaluate what metrics and KPIs we need to be focusing on to drive true organic growth.

It’s no longer enough to be ‘popular’ you also need to be relevant. Not just in terms of the publications you are targeting, but the keywords you want to rank for,  audience interest, and most importantly, brand alignment to the story you are pitching in.

Google is continuously looking to become more intelligent through its use of machine learning and artificial intelligence. It wants to understand web content as a human, and therefore through its use of natural language understanding, it is likely to not just be looking at the anchor text of links in third-party articles, but it is also wanting to understand the wider context of the article that a brand is placed in.

How to ensure your link-building activity is relevant to your brand

The first steps to coming up with relevant content ideas for your digital PR campaign are to:

  1.  understand your client, and
  2.  understand your client’s audience and their needs.

Every good idea will flow from these two pillars.

If Google’s main objective is to show the best content to users through search, then your job is to create content that either supports your client’s product or service or supports their customers.

It is more important than ever to not only create relevant and on-brand content in the written form but also ensure that any supporting assets created (video, images, audio) are also relevant to the target keywords and services or products that the brand sells.

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In addition, it’s important to create content that engages people, to drive further buzz and positive sentiment around the brand, all of which contribute to greater brand awareness and affinity among your potential customers.

How to measure the relevancy of your backlink profile

We now have the technology available to us to be able to understand and assign quantifiable metrics to the relevance of linking coverage (or indeed the relevance of any text-based content) – which allows us to be much more data-driven and targeted when developing digital PR, link creation activity and competitor and marketplace analysis.

For example, natural language understanding tools like Salient, measure the relevancy of both off-page and on-page content. Tools like this help to understand how a search engine is viewing a brand’s content, it not only enables us to identify the gaps in our client’s backlink profile.

At Journey Further, we use this proprietary tool to measure the relevancy of both off-page and on-page content for our clients.

Measuring the relevancy of your backlink profile

We can use this tool to understand how a search engine is viewing a brand’s content, it not only enables us to identify the gaps in our client’s backlink profile but also aids us in optimizing its content on-site. The outcome of which – is a much more focused, effective, and measurable digital PR activity that is better aligned to SEO objectives and that delivers better ROI for clients.

Looking ahead to 2023

Looking ahead to 2023 and beyond, it’s likely that Google will only continue to develop better technology to understand web content.

All digital PR campaigns should reflect this, and where possible, be multi-faceted, not just relying on a single press release to get cut through. We need to be thinking as marketers, not just SEO practitioners, and ensure we are driving as much ROI as possible. Taking a brand plus performance approach to SEO and digital PR will therefore be key.


Beth Nunnington is the VP of Digital PR and Content Marketing at Journey Further, leading Digital PR strategy for the world’s leading brands. Her work has been featured in The Drum, PR Moment, and Prolific North. Find Beth on Twitter @BethNunnington.

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