Learn How Google Shows Us Related Images

2018 Oct. 01

Related Images

You must be searching for related images features and wanting to know how it works. You have come to the exact place. I am here to discuss what is related images and how it works with reverse image search. I will also go through the uses of reverse image search as well as the algorithms used by reverse image search for finding related images.

What are related Images?

Google’s search by image is a feature which uses reverse image search and users can search for related images by just uploading an image or image URL. Google uses the advanced algorithm on the submitted picture by the user and constructs a mathematical model of the same. Then it is compared with millions of images which is in the Google’s database and returns similar or matching results. Moreover, Google also uses the metadata of that picture for searching the images.

About Reverse Image Search

Reverse Image Search is a technique called Content-Based Image Retrieval Query(CBIR) that helps in providing the CBIR system with a sample image for which it will search upon and according to Information retrieval, sample image helps in formulating a search query.

There are some pictures for which we cannot formulate a keyword, therefore, this removes the need for the user to enter the keywords or terms which may or may not produce a relevant search result. Reverse Image Search also helps users to find out content related to that image, popularity of the image, derivative works and discovering the manipulated versions.

Uses Of Reverse Image Search

Some of the uses of reverse image search are as below.

  • It helps to locate the source of the image.

  • Helps in finding the higher resolution of the same picture.

  • It helps in finding the web pages where the image appeared.

  • It helps in tracking the content creator.

  • It also helps in getting the information about the image.

Algorithm Used For Reverse Image Search

Some of the commonly used Algorithm for Reverse Image Search are listed below.

Scale Invariant Feature Transform(SIFT)

Scale Invariant Feature Transform algorithm is about feature detection in computer vision which helps to detect and describe local features of the image.

Maximally Stale Extremal Regions(MSER)

Maximally Stale Extremal Regions(MSER) is used for the method of blob detection in an image. It helps to find out the correspondences between image elements from two different images with a different viewpoint.

Vocabulary Tree

In terms of computer vision, the bag-of-words model (BoW model) can be used for analysis of image through treating features of images as words.


Using related images feature on Google is really easy as you just have to upload an image and it can find you the related images for the same. It really helps when you cannot find a keyword or term for a particular picture. I have walked you through how related image features work and the uses as well. You can also go through the algorithm it uses to find out the related images for understanding it in a better way.

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