Google Image Search Ranking
When it comes to Search Engine Optimisation (SEO) and Google ranking, it’s important not to forget about images. These can be fully optimised to maximise the visibility of your content and boost brand awareness. So, let’s delve into Google image search ranking factors and how machine learning patents will likely play a more prominent role in Google indexing.
How to Rank in Google Image Search
There are currently a number of ways to rank in a Google image search. For example, it’s important to:
- Uploaded unique images of the highest quality that are fully optimised and resized for the web meaning they won’t take too long to load.
- Use a decent sized image that isn’t mistaken for a thumbnail.
- Use descriptive, keyword-rich file names which means Google won’t need to search hard to see the relevancy of your imagery. You should also add valid image Alt Text and an image description.
- Place the images on the first fold of your content/product pages. Recent image search algorithm updates confirmed that the placement of an image matters a lot in terms of search ranking. According to Google, images that feature at the beginning of the page and the one in the middle will receive priority in the search results.
- Create high-quality content as images from quality sites are more likely to be sourced to avoid spammy or poor search results.
Machine Learning and Its Role in Image Search Results
Using all the traditional image search ranking methods is important. However, it’s also essential to be aware of changes to how Google might rank image search results. The search engine giant already uses a machine-learning artificial intelligence system called RankBrain to help sort through its search results, with a new patent application detailing the use of machine learning to rank image search results.
The details are complex and ‘techy’ but the patent essentially revolves around an image search system which includes a training engine. The training engine trains the machine learning model using training data. Every image search result is given a relevance score with the system learning landing page features for the landing page identified by the particular image search result as well as well as image features for the image identified by that image
search result. The image search system then provides the query features, the landing page features and the image features as input to the machine learning model.
What are the Advantages of Machine Learning for Image Search Ranking?
It’s a complicated process on paper, but machine learning could prove increasingly popular with search engines. This is because if Google can rank image search queries based on relevance scores using a machine learning model, it can improve image search relevancy in response to a particular query. This, in turn, enhances the user experience of those making Google searches.
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