Understanding Visual Search: The Technology Behind Image Finder Engines

Visual search is a novel search approach that lets users make queries for image results specifically. Visual search is also known as image search or reverse image search (depending on what kind of input was made). 

Google pioneered image search in 2003 when they noticed that many people were specifically making queries to search for images of celebrities in specific dresses. 

Nowadays, image search is used for a lot more than that. People use it to find the source of an image they found online. They also use it to find images similar to the ones they have, and they use it to find products online and where to buy them. 

That’s all well and good, but how does image search work? What is the magic that makes this kind of search so powerful? We will answer all of that, so stay tuned.

How Does Image Search Work

Currently, there are two major types of visual searches. They are segregated based on the type of input. One is called content-based image retrieval (CBIR), which is used when the user inputs an image as a search query. The other is called text-based image retrieval (TBIR). In TBIR, the user inputs a text-based query, but the intent is to find an image.

Let’s check out how both types work.

Content-Based Image Retrieval (CBIR)

CBIR is a more advanced form of image search. It is typically referred to as “Reverse Image Search” because it finds similar images to the one the user queries with. 

Here’s how CBIR works. 

1. Feature Extraction

When an image is provided to the reverse image search engine, a few things happen. The image is processed and its features are extracted. This is known as feature extraction. 

Feature extraction involves a couple of processes of its own. Let’s break them down.

• Low-level Feature Extraction. These refer to the features of an image, such as its colors, textures, and shapes. In CBIR, these features can be obtained by looking at the image as a whole (global features) or by looking at specific parts (local features). 

Local features are usually more accurate because they don’t change just by flipping or rotating the image. Local features are extracted by identifying specific objects in the image and then obtaining their specifics, e.g., color, shape, and texture.

After all that, a mathematical representation of the features, called a vector presentation, is created. 

• High-Level Feature Extraction. These refer to the semantic features of an image, such as any text written in, the identity of people or places in the image, and other such information. 

Needless to say, this is the kind of information that people are looking for when they do a reverse image search, so this part is pretty important.

High-level feature extraction is done with the help of deep learning convolutional neural networks (CNNs)

2. Similarity Measurement

Once the features have been extracted from the image, they are converted into a vector representation. A vector presentation is a mathematical presentation of an image where each vector specifies a feature of the image.

Now, all image search engines have a database of indexed images. All of these images have their own vector representations. 

In similarity measurement, the search engine compares the vectors for the queried image with those in the database. Various techniques are used for this comparison, for example.

• Euclidean Distance: Measures straight-line distance between two points in feature space.
• Cosine Similarity: Measures the cosine of the angle between two vectors, useful for high-dimensional data.
• Manhattan Distance: Computes the absolute differences across dimensions.

Using these techniques, CBIR search engines can find similar indexed images. But they have thousands of indexed images. How do they find the results so quickly? That’s due to indexing and retrieval techniques.

3. Indexing and Retrieval

To speed up the retrieval of relevant results, image search engines utilize various indexing techniques. The idea is that instead of going through their entire database every time, they focus on a subsection of potentially similar images and only search them.

This can be done via various methods, such as:

• KD trees
• Ball Trees
• Hashing Algorithms

And many more. These indexing techniques make it much faster for search engines to find and retrieve images for any query.

Text-Based Image Retrieval (TBIR)

Keywords and metadata, as well as image descriptions/alt text, are used to find the right image using a text query.

TIBR is a different method of image search. It is much simpler and is quite similar to the standard text search. The only difference is that you are specifically looking for an image instead of something else.

TIBR relies on textual data associated with an image and compares that with indexed images to find matches. The textual data includes things like the following:

• File names
• Image captions and descriptions
• Alt text
• Tags and keywords
• EXIF data

The thing is that all the textual data is usually manually added to an image or generated automatically. As such, the results of a TIBR are highly dependent on the accuracy of the textual data associated with an image.

Now, let’s see how TIBR Works.

TIBR Working

In TIBR, there are three major steps.

User Query. The user enters some descriptive words/phrases into the search engine to look for an image.

• Text Matching. The search engine picks out keywords from the query and starts to match them with known text fields of indexed images.

• Ranking: Images are ranked by relevance to the query using algorithms like TF-IDF (term frequency-inverse document frequency) or modern NLP models. Then the highest-ranked images are shown to the user.

TIBR is often used in stock image platforms and picture boards where consistent tagging is maintained. However, in any scenario where tagging is lax, CIBR is always better.

Where is Image Search Used? 

Image search has evolved far beyond finding celebrity photos or random internet memes. Today, it's a practical tool used in various industries and for a wide range of real-world applications, such as the following.

• E-commerce: One of the most common uses of visual search today is in online shopping. People can upload a picture of an outfit or gadget into a reverse image search tool, and the system returns links and images of similar products available for purchase. This is particularly helpful in fashion, home decor, and electronics.
• Education and Research: Students and academics use image search to find diagrams, maps, or historical photos. Researchers can also trace the source of an image or analyze the visual content for citations.
• Law Enforcement and Security: Facial recognition and reverse image search technologies are used to match images of suspects, track digital evidence, or identify fake profiles on social media platforms. They also help identify cyber scams and other online threats.
• Healthcare: Medical professionals use CBIR search engines to compare X-rays, MRIs, or other medical imagery with databases to aid diagnosis by identifying visually similar conditions.
• Art and Media: Artists and content creators use image search to verify originality, track reposts, or find higher-quality versions of their own work. It's also helpful for sourcing stock photos or finding visual inspiration.
• Travel and Real Estate: Uploading an image of a landmark or property can help users find more information, similar destinations, or even rental listings.

So, those are some of the well-known uses of reverse image search today.

Conclusion

Reverse image search is a powerful tool that is often much better than a simple text search. The technologies available today have improved the efficiency of text search considerably. 

You can use it to find similar images or the source of an image that is considered exceedingly rare. The best thing about reverse image search is that it is free, and you can use it anytime. So, start using it for your own good, be it for shopping, research, or studying.