Search Products By Image API: Efficient And Accurate

In the ever-changing realm of electronic business, innovation is an absolute must. Text-based inquiries are the mainstay of traditional search technologies, which are proving to be inadequate in satisfying the needs of contemporary customers. A significant change has occurred with the introduction of visual search technology, which offers consumers more engaging and natural ways to find things. This blog post explores the constraints of text-based search, the increasing significance of visual search in e-commerce, and the technological developments that made Search Products By Image API products like SightScout possible.

Defining SightScout

E-commerce platforms can incorporate visual search functionality into their websites and applications with the use of a specialist tool called Search Products By Image API. Users can upload an image to this API to get a list of things that are for sale that seem similar. Advanced machine learning algorithms form the basis of the technology, which compares the uploaded image to a database of product photographs after analysis. A prime example of such an API is SightScout, which provides effective and precise visual search solutions.

The technology behind a Search Products By Image API combines several advanced elements, including image recognition, machine learning, and data processing. The process begins with the image recognition algorithm, which identifies key features and patterns within the uploaded image. Machine learning models then compare these features against a vast database of product images to find the best matches. This complex interplay ensures that the search results are both relevant and accurate, enhancing the overall user experience.

Different Endpoints

Save Record or Asset in Index

You can submit the assets or photos that users will search for using this Endpoint. Stated otherwise, the search library constitutes the assets. If it's an e-commerce site, for instance, you have to post the same number of photographs as your products. Additionally, you can post several images for each product.

Body

POST https://sightscout.net/api/v1/indexes/YOUR_INDEX_HOST/saveRecord
    {
        "objectID": "your-object-id",
        "image_url": "value1",
        "product_id": "value2",
        "meta_data": "{\"color\":\"azul\",\"talle\":\"M\",\"brand\":\"ExampleBrand\"}"
    }

Search Endpoint

You can send a picture to the Search Endpoint and return similar images (assets) back with a score (from 0 to 1) indicating the highest similarity and 0 the lowest similarity).

Body

POST https://sightscout.net/api/v1/indexes/YOUR_INDEX_HOST/search
    {
        "image_url": "https://example.com/image.jpg"
    }

Delete a Record by Object ID

You can remove a record or asset from your index using this Endpoint. Please remember that when you construct your index, you need to change YOUR_INDEX_HOST with the Index Host that SightScout will provide you through the online interface.

Find Object ID in Index

You can obtain all the details associated with an Object ID, including meta_data, asset_url, type, engine, created_at, updated_at, and product Id, by using this Endpoint.

Why Text-Based Search is Insufficient

In e-commerce, text-based search engines have long been the norm, but they have a lot of drawbacks. Often, descriptive terms fall short of encapsulating the subtleties of a product's design. For example, a buyer might find it difficult to explain the precise style of a piece of furniture or a dress's distinctive design. Users may become irate and miss out on sales opportunities as a result of this deficiency. By enabling consumers to easily upload a photo and identify visually related products, visual search bridges the gap between inspiration and purchase, overcoming these difficulties.

Artificial intelligence and machine learning have advanced significantly along the path of image identification technologies. The accuracy and scalability of early picture recognition systems were problematic. But recent advancements have produced complex algorithms that can classify and recognize photos with remarkably high accuracy. In particular, deep learning models have been crucial in improving visual search engines' accuracy and enabling their commercialization.