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A step-by-step guide for using image similarity as a service. Find similar items with accurate & fast API for Image Search.
With the service Image Similarity added to the Ximilar App, you can build your own visual similarity engine powered by artificial intelligence in just a few clicks, with several lines of code. Similarity search enables companies to improve the user experience significantly and increase revenue with smarter management of their visual data.
The technology behind image similarity is robust, reliable & fast. Built on state-of-the-art (SOTA) AI models and vector databases, you can search millions of images/products in milliseconds. It is used by big e-commerce players as well as small startups for showing visual alternatives or finding products with pictures. Some of our customers have hundreds of millions of images in their collections and do more than 100 million requests per month. Let’s dive into building a superfast similarity search service for your web.
Image Similarity, or image similarity search, is a visual AI service comparing, grouping, and recommending visually similar images. For example, a typical use case is a product recommendation of similar items in e-shops. It can also be used for reverse image search, where the query is an external image and the results are images from the collection. This approach gives way more accurate results than searching by tags, labels and other attributes.
Ximilar is using state-of-the-art deep learning models for all visual search services. We build our own indexing & searching technology that can run both as a service or on your hardware if needed. The collections can be focused either on product photos, fashion, image matching, or generic photos (stock images).
Here are several features of the Image Similarity service that we think are crucial:
According to this research by Deloitte, merchandising with artificial intelligence is more and more relevant, and recommendation engines play a vital part in it. Here are a few use cases for visual similarity engines:
Recommending products to your customers has several advantages. Firstly, it creates a better user experience and helps your customers find the right products faster. Secondly, it instantly makes the purchase rate on your web higher. This means a win on both sides – satisfied customers and higher revenue for you. Read more about customer experience and product recommendations in our blog post on fashion search.
So let’s take a look at how to easily build your own similarity search engine with the Ximilar platform. The first step is to log in to the Ximilar App. If you don’t have an account, then sign up – it’s free and takes just a minute. After that, on the Dashboard, click on the Visual Search tile and then the Image Similarity service. Then go to the Collections in the left menu and click on Create New Collection. It will show a pop-up with different collection types from which you need to select one.
The collection is a space where you upload your images. With this collection, you are performing queries for search. You can choose from Generic Photo Collection, Product Photo Collection, Dominant Colors Similarity, and Image Matching. Clicking on one of the cards will create a collection for your account.
Each of these collection types is suitable for different types of images:
For this example of real estate, I will use a Generic Photo Collection. The advantage of Generic Photo Collection is that it also supports searching images via text input/query. We usually develop custom similarity models for real estate, when the customers need specific and more accurate results. However, for this simple use case, the generic real estate model will be enough.
First, we need to prepare a text file with JSON records. Each record represents an image that we want to store/insert into our collection. The key field is "_url" with the image URL. The advantage of the _url is that you can directly see and inspect the results via app.ximilar.com.
You can also optionally send records with base64 data, this is great if your data are stored locally on your computer. Don’t worry, we are not storing the whole images (data or base64) in the collection database, just URLs with all other metadata present in the records.
The JSON records look like this:
{"_id": "1_1", "_url": "_URL_IMAGE_PATH_", "estate_id": "1", "category": "indoor", "subcategory": "kitchen", "tags": []}
{"_id": "1_2", "_url": "_URL_IMAGE_PATH_", "estate_id": "1", "category": "indoor", "subcategory": "kitchen", "tags": []}
...
If you don’t have image URLs, you can use either "_file" or "_base64" fields for the image data (locally stored "_file" data are automatically converted by the Python client to base64). The image similarity engine is indexing every record of the collection by extracting a representation from the image by a neural network model. However, we are not storing the images in our engine. So, only records that contain "_url" will be visualized in the Ximilar App.
You must store unique identifiers of each image in the "_id" field to identify your images in the collection. The value of this field must be a string. The API endpoint for searching is returning this _id values, that is how you get the results for visual search. You can also store additional fields for every JSON record, and then you can use these fields for filtering, grouping, and tuning the similarity function (see below).
The next step requires a few lines of code. We are going to insert the prepared images into our collection using our python-client library. You can install the library using pip or directly from GitLab. The usage of the client is very straightforward and basically, you can just use the script tools/collections/insert_json_records.py:
python insert_json_records.py --type generic --auth_token __YOUR_TOKEN__ --collection_id __COLLECTION_ID__ --path /path/to/the/file.json
You will find the collection ID and the Authorization token on the “collection page” in the Ximilar App. This script will run for a few minutes, depending on the size of your image dataset.
That was pretty easy, right? Now, if you go to the collections page, you will see something like this:
All images from the JSON file were indexed, and now you can inspect the collection in the Ximilar App. Select the Similarity Search in the left menu of the Image Similarity service and test how the similarity works. You can specify the query image either by upload, by URL, or your IDs, or by choosing one of the randomly selected images from the collection.
Even though we have indexed just several hundred images, you can see that the similarity engine works pretty well. The first image is the query image and the next images are the k-nearest to the query image:
The next step might be to integrate the service into your application via API. You can either directly use the REST API for searching visually similar images or, if you are using Python, we recommend our Python SDK client like this:
Product Description 
Card Price Search # pip install ximilar-client
from ximilar.client import SimilarityPhotosClient
client = SimilarityPhotosClient("_API_TOKEN_", "_COLLECTION_ID_")
# search k nearest items
client.search({"_id": "1"}, k = 3)
# search by external image
client.search({"_url": "_URL_PATH_"})
The search for visually similar images can be combined with filtering on metadata. This metadata can be stored in the JSON, as in our example with the "category" and "subcategory" fields. In the API, the filtering is specified using a MongoDB-like syntax – see the documentation.
For example, let’s say that we want to search for images similar to the image with ID=1_1 that are indoor photos made in a kitchen. We assume that this meta-information is stored in the “category” and “subcategory” fields of every JSON record. The query will look like this:
client.search({"_id": "1_1"}, filter={"category": "Indoor", "subcategory": "Kitchen"})
If we know that we will often filter on some fields, we can specify them in the “Fields to index” option of the collection to make the query processing more efficient.
Often, your data contains several photos of one “object” – a product or, in our example, real estate. Our service can group the search results not by individual photos but by product IDs. You can set this in the advanced options of the collection by specifying the name of the real estate in the Product ID field, and the magic will happen.
The image similarity is based purely on the visual content of the image. However, you can use your tags (labels, keywords) to enhance the similarity search. In the example, we assume that the data already contains categories, subcategories, and tags. In order to enhance the visual similarity search with tags, you can fill the “tags” field for every record with your tags, and also use method /v2/visualTagsKNN. After that, your search results will be based on a combination of visual similarity and keywords.
If you don’t have categories and tags, you can create your own photo tagger through our Image Recognition service, and enrich your image data automatically before indexing. The possibilities of image recognition models and their combinations are endless, resulting in highly customizable solutions. Read our guide on how to build your own Image Recognition API.
You can build several models:
The real estate photo similarity search is only one use case of visual similarity from many (fashion, e-commerce, art, stock photos, healthcare…). We hope that you will enjoy working with this service, and we are looking forward to seeing your projects based on it. Thanks to our developers Libor and Ludovit, you can use this service through the frontend app.
Visual Similarity service by Ximilar is unique in terms of search quality, speed performance, and all the possibilities of the API. Our engineers are constantly upgrading the quality of the search, so you don’t have to. We are able to build custom solutions suitable for your data. With multiple collections, you can even A/B test the performance on your websites. This can run in our cloud as SaaS or in your warehouse! If you have more questions about pricing, and technical details, or you would like to run the similarity search engine on your own machines, then contact us.
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