 and for treatment.
Bassos was seated beside an obese man, who “encroached into his seat,” on the flight from the United Arab Emirates to Sydney, which lasted for over 14 hours, on October 2010 flight.
To evade physical contact with the fat passenger, who was constantly spilling and coughing, the man had to crook his body. Approximately five hours later he felt backache and asked for a seat change.
Bassos’ request was denied at first, as there were not vacant seats in the airplane. But when the pain became unbearable, the man was permitted to move to the crew seat in the tail of the plane. However, Bassos was later asked to return to his initial seat two times.
Etihad airline representatives claimed in response that sitting next to an overweight person on a crowded flight is common or at least expectable, according to the Courier Mail. They concluded that Barros’ case cannot be considered as an accident.
The airline expected to have the charges lifted this week, as medical examination results have been released. James Bassos will finally face a medical assessment by the end of this year.
Google provides an AI image classification tool that analyzes images, classifies content, and assigns labels to images.
This tool is intended as a demonstration of Google Vision that can scale image classifications on an automatic basis, but it is a stand-alone tool for seeing how image detection algorithms display images and what they relate to. Can be used as.
Even if you don’t scale image detection and classification using the Google Vision API, this tool provides an interesting view of the capabilities of Google’s image-related algorithms. This allows you to upload images and see how Google’s Vision algorithm classifies images...
This tool shows Google’s AI and machine learning algorithms for understanding images.
It’s part of Google’s CloudVision API suite, which provides vision machine learning models for apps and websites.
Does the Cloud Vision Tool reflect Google’s algorithms?
This is just a machine learning model, not a ranking algorithm.
Therefore, it is impractical to expect to use this tool to reflect something about Google’s image ranking algorithm.
However, it’s a great tool for understanding how Google’s AI and machine learning algorithms understand images, and provides educational insights into how advanced today’s vision-related algorithms are.
You can use the information provided by this tool to understand how the machine understands the content of the image and how exactly the image fits the entire topic of the web page...
Why are image classification tools useful?
Images can play an important role in search visibility and CTR from different ways that web page content is displayed throughout Google.
Potential site visitors investigating the topic use images to navigate to the appropriate content.
Therefore, using attractive images related to search queries can help you quickly tell that a web page is related to what a person is searching for, within a particular context.
Google Vision tools provide a way to understand how algorithms display and classify image content.
Google’s guidelines for image SEO recommend the following:
“High-quality photos are more appealing to users than blurry and obscure images. Also, sharp images are more appealing to users with the resulting thumbnails and are more likely to get traffic from users.. “
If your vision tools can’t determine the content of your image, potential visitors to your site may have the same problem and decide not to visit your site.
What is Google Image Tools?
This tool is a way to demonstrate Google’s CloudVision API.
The Cloud Vision API is a service that allows apps and websites to connect to machine learning tools and provides scaleable image analysis services.
Using the standalone tool itself, you can upload images and see how Google’s machine learning algorithms interpret them.
Google’s Cloud Vision page describes how to use the service as follows:
“CloudVision makes it easy for developers to integrate visual detection features into their applications, such as image labeling, face and landmark detection, optical character recognition (OCR), and explicit content tagging. increase.”
Here are five ways Google’s image analysis tools can classify uploaded images:
face. object. label. Property. Safe search.face
The Face tab provides an analysis of the emotions represented by the image.
The image below is the person described as confused, but it’s not really an emotion.
With a 96% confidence score, AI describes the emotions expressed on the face as surprised.
Composite image created by the author, July 2022. Images provided by Google CloudVision API and Shutterstock / Cast Of Thousands Objects
The Objects tab shows objects in the image, such as glasses and people.
This tool accurately identifies horses and people.
Composite image created by the author, July 2022. Images provided by Google CloudVision API and Shutterstock / Lukas Gojda Labels
[ラベル]Tabs show not only Google-recognized image details such as ears and mouth, but also conceptual aspects such as portraits and photos.
This is especially interesting because it shows how deeply Google’s image AI can understand the content of an image.
Composite image created by the author, July 2022. Images provided by Google CloudVision API and Shutterstock / Lukas Gojda
Does Google use it as part of its ranking algorithm? That’s unknown.
The property is the color used in the image.
Screenshot of Google Cloud Vision API, July 2022
On the surface, the point of this tool is not clear and may seem impractical.
But in reality, the color of the image can be very important, especially for the image of interest.
Images with a very wide range of colors may indicate an inadequate selection of bloated images. This is something to be aware of.
Another useful insight into images and colors is that images with darker color ranges tend to have larger image files.
From an SEO perspective, the Properties section can help identify images across your website that can be exchanged for images that aren’t bloated in size.
Also, the color range of a mute or grayscale featured image may need attention, as featured images without vibrant colors tend not to appear on social media, Google Discover, or Google News.
For example, vibrant featured images can be easily scanned and have a higher click-through rate (CTR) when viewed in search results or Google Discover because they are more visible to the eye than images that are muted and faded. There is sex. In the background.
There are many variables that can affect image clickthrough rate performance, but this provides a way to scale up the process of auditing images across your website.
eBay conducted a product image and CTR survey and found that images with a lighter background tended to have a higher CTR.
Researchers on eBay said:
“In this white paper, we found that the functionality of product images can affect a user’s search behavior.
We have found that some image features correlate with the CTR of product search engines, and these features help model clickthrough rates in shopping search applications.
This survey can provide an incentive for sellers to submit better images of the products they sell. “
By the way, using bright colors for featured images can help increase your CTR for sites that rely on traffic from Google Discover or Google News.
Obviously, there are many factors that affect CTR from Google Discover and Google News. However, images that stand out more than other images may be useful.
Therefore, understanding the colors used using the Vision Tool will help you to audit the image in scale.
SafeSearch shows how images are ranked for unsafe content. The following is a description of the images that may be unsafe.
adult. Impersonation. Medicine. violence. Lacy.
Google Search has filters that rate web pages for insecure or inappropriate content.
That’s why the SafeSearch section of the tool is very important. This is because if an image unintentionally triggers a SafeSearch filter, it can fail to rank potential site visitors looking for content on your web page.
The screenshot above shows the rating of a photo of a racehorse on a racetrack. This tool accurately identifies that the image does not contain medical or adult content.
Google Vision has a great ability to read the text contained in photos.
The Vision Tool can accurately read the text in the image below.
Composite image created by the author, July 2022. Images provided by Google CloudVision API and Shutterstock / Melissa King
As you can see above, Google has the ability to read words in images (through optical character recognition, also known as OCR).
However, this does not indicate that Google is using OCR for search ranking purposes.
In fact, Google recommends using the words around the image to understand the content of the image, and even for images that contain text, the content of the image depends on the words around the image. You may understand. It is about and related.
Google’s guidelines for image SEO repeatedly emphasize the use of words to provide image context.
“Adding image context can make the results much more useful and improve the quality of traffic to your site.
… Place the image as close to the relevant text as possible.
… Google extracts information about the subject of the image from the content of the page…
… Google uses alternative text along with computer vision algorithms and page content to understand the subject of the image. “
It’s very clear from Google’s documentation that Google relies on the context of the text around the image to understand what the image is.
Google’s Vision AI tool provides a way to test drive Google’s Vision AI. This allows publishers to connect to it via an API and use it to scale image classifications and extract data for use within their site.
