Google Image Search, to find useful photos for content as well

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We can call it a parallel SEO, which aims at optimising a specific type of content: image search is becoming an interesting challenge not only for those who provide the service (i.e. image search engines), but also and above all for sites that want to appear in positions of visibility and exploit these resources to win new organic visitors. As we probably experience every day, finding the perfect image to go with a content can often be a challenge, but it is very useful in order to succeed in the goal of winning over the reader: in our support come various image search engines, starting with Google’s service, but also the mechanism of reverse image search, with which we can also deepen the context around the photo.

Image search, what it is

It has certainly happened to every one of us to do an image search or a search for images, i.e. to use a classic search engine with the express purpose of displaying b photos to be precise.

According to some estimates, in fact, almost 20 per cent of all search queries on Google refer to images and people search for millions of images online every day.

Until not too long ago, the easiest way to perform this task was just to use a normal search engine and type query+image (or query+photo) into the search bar, but thanks to the overall evolution that technology is undergoing, we now have even faster, and above all more effective, ways to search for images – that is, to use image search engines or to take advantage of the reverse search functionality.

In all cases, however, we still have to enter a relevant keyword and cross our fingers, hoping that the image we have in mind actually exists on the Internet and the Web. If we are lucky (if we have used the right query and chosen the best search engine) it will only take a few seconds to find the resource and possibly use it on our site, where possible by licence. In this regard, it is worth a quick reminder that most images found online are normally subject to copyright and therefore we cannot simply download or copy them for use on our blog or website, but to our rescue come the various stock photo sites that make available high-definition images, often also free of charge, licensed under Creative Commons.

The SEO opportunities of image search

It is already clear from what has been written why it can be important for those working in the digital field to learn how to do an effective image search: multimedia content is now a fundamental asset for web pages, to be taken care of as much as text content, and the right SEO optimisation of images must be implemented to fully exploit their value.

To summarise, hosting unique, contextualised and content-consistent images on site pages – and tagged with the right keywords – can increase the chances of ranking such assets on search engines and, in concrete terms, the chances of users finding these images and landing on our site through a general image search or a reverse image search.

Thus, media is a possible source of additional organic traffic for our business, as well as an additional leverage to outperform competitors, and more generally, media is becoming more and more important with each passing year, playing a crucial role in enhancing the user experience and responding to the user’s search intent.

Image search engines, the allies for finding photos online

An image search engine is simply an online portal that stores and indexes image files, labelled according to specific keywords or tags: today they are more advanced than ever and allow the user to refine the search for the desired image through advanced search filters and other tools.

Image search works exactly like text search, with the difference that the SERP that appears after entering a query will not consist of clickable blue links, but of thumbnail preview images corresponding to the search keywords.

In this way, image search becomes much simpler because we avoid having to browse through hundreds of similar pictures before locating the appropriate one that may then, unfortunately, not be accessible or freely usable.

Through image search engines, we can find a photo to be correlated with text content, with the correct usage rights, or a high-resolution image to be used in a marketing campaign, or simply a photo to be used as desktop wallpaper: in all possible cases, there are plenty of tools to help us.

How image search engines work

The Web offers many different options for image search, from general search engines with an image search function to more specific search engines dedicated to image browsing and indexing. The latter, in particular, are very useful portals that help millions of people find high quality images without having to waste time scouring the web for the exact photo they need.

The success of a search engine is based on its ability to serve relevant images, and thus to correctly match the indexed resources to the query people are looking for, also by means of tags that define the ‘context’ and help filtering. Conventionally, tagging makes it possible to distinguish the type of image (photograph or graphic), the colours (black and white, colours, dominant colour), the category (to simplify the search) and the content (the actual tags, like those on blogs in short).

As said, there are many image search engines and it is not easy (nor perhaps possible) to identify the absolute best, because each system has its advantages and disadvantages and everything depends on our needs. The most famous and most widely used is obviously Google Images, which has on its side the strength of the classic search system, but also the combination of a powerful general image search and reverse search functionality.

Not to be overlooked, however, is the alternative represented by Bing, which, with its attractive visual interface and easy-to-understand filter options, positions itself as a strong competitor for general image searches, just as the old Yahoo! or Yandex also offer their own image search engine.

Then there are more specific tools that focus precisely on visual resources: for instance, Pinterest (which is precisely an image-based platform) has its own visual search tool that allows one to browse through results visually similar to the image that has caught one’s eye, selected from within a rather large image database thanks to pins created by users. Even Flickr, although now in a waning trend, is still a possible mine of original images from amateur and professional photographers who share their work on the platform, while TinEye offers more refined and often better suggestions than Google’s reverse image search.

In any case, learning how to use image search engines efficiently will be of great help to us and, often, we will only find the best result by launching search queries on several tools and comparing their answers.

Reverse image search, what it is used for

In addition to the classic text search, however, we now have another possibility that we have already alluded to, namely the reverse image search, which allows us to find photos similar to one that we have in our computer archive or that has caught our eye online.

In a nutshell, reverse image search engines are able to find the source file of an image, helping us to trace the original source of the image after uploading a similar and related one as a search query.

There are many possible use cases for these tools: we can, for instance, start from a small or blurred image to display (and use) the high-resolution original, or launch a search for a product of which we have no information other than a photo (and thus find details of the name, brand and other useful information), or even delve into the context of an image.

Reverse image searches are particularly useful for webmasters and content creators who need to find a similar image of higher quality or a source file. Among the advantages of these systems are the possibility of verifying the origin of an image (and possibly also its online dissemination, which is particularly effective for authenticating profiles of people, news and event images), monitoring copyrighted images (discovering precisely which sites have used that media content, with or without attribution) and precisely finding similar images, i.e. better shots or options for an image.

Amongst the disadvantages, we must say above all that the reverse image search does not always work, because it may happen that the function is executed without obtaining results, and this depends on various factors – site hosting the image preventing media content indexing, for example, or data centre slightly out of sync or search engine not particularly effective in recognition.

How to perform a reverse image search with Google

Besides the aforementioned TinEye tool and other similar systems, the most famous and most widely used reverse image search engine is undoubtedly Google with its Google Reverse Image Search, which in practice is an alternative method for finding images on Google from photos, valid when we are in doubt about the most appropriate terms to use in the query or when we want to learn more about an image we have seen online or saved on our hard disk.

Launched as a feature in 2011, Google reverse image search is really easy to use, both from desktop and mobile, because it works on Safari, Firefox and Chrome, with obvious advantages when using Google’s internal browser. For instance, Chrome users can simply right-click on an image anywhere within the window and select ‘search for this image on the Web’ (now ‘search for this image with Google Lens‘) and display a SERP that returns what the algorithms consider to be the ‘best guess for this image’, as well as pages that include matching images.

From the home page of Google Images, we can launch a search by clicking on the camera icon and selecting one of the two available tabs: intuitively, ‘paste image URL’ allows us to launch a search for an image already present online, while ‘upload an image’ allows us to upload a resource saved in our offline archive (unlike Tineye, there is no limit to the size of images that can be uploaded to Google).

In both cases, after clicking on ‘Search by image’, we will see the uploaded photo at the top of the page together with some suggested keywords: next to the image, Google will inform if there are other image sizes available for download. From here we can explore similar images or check the websites that contain the image.

If we want to launch a reverse image search from a smartphone, the procedure is not very different: we open the Google mobile app and click on the camera icon on the right. The system will ask us for access to Lens in order to directly frame an object with the camera and find related results – an approach that will be increasingly central to search and which is also the basis of the Multisearch system presented at the last Google I/O 2022.

The technical features of Google Image Search

We have made direct reference to Google Images because this system is not only the most widely used, but also probably the most avant-garde in the field of image search engines, and some news and studies confirm this – as well as make clear the attention that the Californian company is devoting to the subject, especially on the technology side.

Google speeds up image loading

In this sense, Google has for some time now officially activated a useful feature to improve the performance of multimedia content, making a lazy image loading system available on Google Chrome Canary (the experimental version of the browser available to developers). Already in her speech at Google I/O 2019, Chrome Product Manager Tal Oppenheimer said that modern sites are more visual than ever and use a lot of high-resolution images; however, loading all those images individually can slow down the browser and waste the user’s connection data, because images that the person would not actually see are also loaded.

For this reason, the Googler continued, it is often better to allow only the images that are needed to be loaded, a technique known as lazy loading; believing that it can be complicated for developers to use stand-alone JavaScript solutions for this purpose (and, at the same time, keep the quality of the experience high for their business), Google designed a solution to simplify and speed up the loading of images from their site.

Solutions for developers in Chrome Canary

In practical terms, by selecting a flag in Chrome Canary (and activating the tick on loading=’lazy’), it is possible to test the new image loading experience after adding the new loading attributes to the image tags; Chrome takes care of the remaining part to determine when to load the images, evaluating factors such as the user’s connection speed, and also checks the first two kilobytes of the different images on the site to add a placeholder in the right size.

The end result of the system is a smoother experience for websites with heavy images, which can make them usable more quickly without the need to write additional code.

Le parole di Google sulle Immagini

Google has the best image recognition system

Certifying the competitive advantage of the Mountain View company’s search system is, on the other hand, a study by Perficient Digital, which compared the performance of Google Images with competitors from Microsoft, Amazon and IBM in the study of image recognition. According to the report, Google Vision technology performs better than those recorded by competitors including Amazon AWS Rekognition, IBM Watson and Microsoft Azure Computer Vision in recognising images adequately, with an accuracy exceeding 80 per cent. A figure that, surprisingly, is not too far from the performance of the human sample used in the tests (just under 90 per cent).

Lo studio sul riconoscimento di immagini


Perficient Digital’s study

The test followed this methodology: two humans collected and tagged 2000 images in four distinct categories including people, landscapes, graphics and products, distributing around 500 images in each section and assigning five tags to describe each photo. Perficient’s software ran all images through each of the APIs of the analysis technologies listed above, examining the results with a unique set of tags for each image in each API; when each tag was assigned a value, the next image was presented.

As mentioned above, Google Vision proved to be the most advanced software currently and the most capable of correctly recognising elements in images. This information can be quite useful to understand the state of work on the refinement of these details, and it is easy to imagine that the technology integrated in the search engine is even more evolved than the one presented to the public with the API.

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