Image Similarity Search

Do not restrict your users to search by keywords & attributes. Let them find visually similar photos or products, quickly and automatically. Seeing similar alternatives keeps users happier and increases conversion rate.
  • Search
  • General Photos
  • Product Photos

Visually Similar Fashion

Fashion e-shops and integrator sites often display related products in each item page. With Ximilar, these products are generated automatically as visually similar items. Our Visual Similarity engine is tuned directly for fashion and can do this instantly. Your collection uploaded to Ximilar, and a single API call gets you the most similar images to an item in a particular category. Our Neural Network processes every image and our powerful similarity search indexes can search millions of items in a fraction of a second.

Given Product

Big Product

Capabilities

  • 10s of Millions of Product Images
  • Results available in 100s of milliseconds

Visually Similar Products

ProdSimilar1
85%
ProdSimilar2
79%
ProdSimilar3
77%
ProdSimilar4
75%
ProdSimilar5
71%
ProdSimilar6
66%
ProdSimilar7
63%
ProdSimilar8
62%
ProdSimilar9
59%
ProdSimilar10
56%
ProdSimilar11
53%

Try it yourself: See demo

Similar Photos & Search by Image

E-commerce, stock photo agencies and big data collectors typically search their collections by tags, labels and other attributes. Ximilar provides complementary approach — searching for images that are visually similar to a given image. Your customers can see alternatives from the long tail they would never find by attribute filters. The query can be an external image and you can reverse search your collection for the same or similar images. Our similarity models are tuned especially for generic images and fashion product photos.

Uploaded Photo

Big Photo

Capabilities

  • 10s of Millions of Images
  • Results available in 100s of milliseconds

Visually Similar Imagery

Similar1
85%
Similar2
79%
Similar3
66%
Similar4
71%
Similar5
79%
Similar6
77%
Similar7
85%
Similar8
67%
Similar9
68%
Similar10
83%
Similar11
74%
Similar12
65%

Try it yourself: See demo

Various Layers of Similarity

Ximilar measures the mutual similarity of images from several viewpoints — semantic content (what is on the image), color impression, keywords & tags, etc.  For specific areas, like fashion, our similarity model learns from hundreds of thousands pairs of similar fashion products. We can also create a specific similarity function for you — just talk to us about your use case.

We are Experts
Ready to Help You Out

Machine Learning is our passion. We know all the ins & outs of technologies that might help you level-up your business using full or partial automation. Feel free to contact us and lets speak about what we can do for you.

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Technology Stack

We use state of the art neural network models & machine learning techniques. Constantly trying to improve the technology so you always have the best quality available. Each model has millions of parameters which can be processed by CPU or GPU. Our intelligent algorithm picks from several models & uses the best performing.

  • Tensorflow-Logo
  • Docker-Logo
  • Nvidia-Logo
  • IbmCloud-Logo
  • Python-Logo

API Documentation

We take care of the complexity behind and wrap it in a few lines of code.

full documentation 
import requests 
import json 
 
url = 'https://api.ximilar.com/similarity/photos/v2/visualKNN/' 
headers = { 
    'Content-Type': 'application/json',
    'Authorization': "Token __API_TOKEN__", 
    'search-collection': "my_collection"
} 

 
data = { 
    'query_record': {'_url': __IMAGE_URL__ },
    'k': 10
} 
 
response = requests.post(endpoint, headers=headers, data=json.dumps(data)) 
if response.raise_for_status(): 
    print(json.dumps(response.json(), indent=2)) 
else: 
    print('Error posting API: ' + response.text)
$curl_handle = curl_init("https://api.ximilar.com/similarity/photos/v2/visualKNN");

$data = [
    'query_record' => [ '_url' => 'https://bit.ly/2IymQJv' ],
    'k' => 10
];

curl_setopt($curl_handle, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($curl_handle, CURLOPT_POSTFIELDS, json_encode($data));
curl_setopt($curl_handle, CURLOPT_RETURNTRANSFER, true);
curl_setopt($curl_handle, CURLOPT_FAILONERROR, true);
curl_setopt($curl_handle, CURLOPT_HTTPHEADER, array(
        "Content-Type: application/json",
        "Authorization: Token __API_TOKEN__",
        "search-collection: my_collection",
        "cache-control: no-cache",)
);

$response = curl_exec($curl_handle);
$error_msg = curl_error($curl_handle);

if ($error_msg) { // Handle error
    print_r($error_msg);
} else { // Handle response
    print_r($response);
}
curl_close ($curl_handle);
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