How to build Visual Image Search in E-Commerce

In today’s competitive e-commerce world, providing a seamless shopping experience is crucial. Visual image search enhanced the user experience.

This tech allow users to search the product from images instead of text. Which make easier to user to find exactly they want.

Here’s a guide on how to build a visual image search for your e-commerce platform.

Basic flow of Visual Image Search

Visual image search using Computer Vision and Deep Learning to identify the products in images. User uploads the image, system analyse it and retrieve the similar product from the inventory.

These processes involve: image processing, feature extraction and similarity search.

Frameworks Used in Visual Image Search

• Preprocessing: We can pre-process the image like – RGB to BGR , resizing, normalization, etc., by OpenCV or Tensorflow or scikit-image.
• Feature Extraction: Extract the features of image by CNN models, where we can use Tensorflow or pytorch or keras.
• Similarity Search: Here we use a vector database like chromadb for similarity search.

Steps to Build Image search for e-commerce

1. Build Feature Extraction Model

• First, we build a feature extraction model on python, for this we use TensorFlow. Select the best CNN model from Tensorflow.
• Apply transfer learning steps like layer freezing and removing the top dense layer. After this initialize the model.
• Prepare the CNN model preprocessor, each model has its own different preprocessor. You can find it in the Tensorflow library.
• Make the function resize the image as per the CNN model input requirement, in most cases it requires a 224*224*3 image size.
• Now preprocessing and model are ready, so assemble them together in a function where the image goes through resize, then preprocess and model.predict(image).
• We got features or vector embeddings.

2. Setup Vector Database

• Second, set up a vector database like Chromadb to store the feature data in vector-db.
• For Instance, we use chromadb
• ChromaDB Setup process
• git clone https://github.com/chroma-core/chroma.git
• docker-compose up -d –build
• Now chromadb is working on 8000 port
• Read the further chroma docs for API endpoints

3. Collecting and Product Data Ingestion

• Collect all product data, their image, id , description, meta, etc.
• Ingest or store the data in chromadb
• Create image embedding of all products through feature extraction API and save in chromadb or another vectordb with their product ID, and description.
• Now vector database is ready for search.

4. Build Search API

• In this step, you have to build an upload option for the user to upload the image to search for a similar product.
• You can use FLASK or FAST API for build feature extraction model API in Python.
• Also have to build upload API on the front end.
• This API is connected to the feature extraction API and VectorDB.
• The flow of this API is Upload Image -> Feature Extraction API -> VectorDB API -> Result

5. Visual Image Search Result

• For instance, the user uploads an image and searches for a product, the image goest to the features extraction API and creates the Image embeddings.
• Now we trigger similarity search in vector database by these image embeddings.
• We can show a similar result from vectordb result.
• In vectordb result, we got the similarity score or distance score of the product, product ID, and their description.
• Now we have the product IDs from the vectordb result, so now we can show the all similar products through the IDs.

In this way, you can build your Visual Image Search for e-commerce.

This procedure is complex, you must know about Deep learning, Computer vision, and API development.

Instead of this, you can check out our AI Image Search modules.

AI Image Search modules

• Magento 2 AI Image Search
• Prestashop Image Search
• WooCommerce AI Image Search
• Odoo Website Product Image Search

Conclusion

In conclusion, implementing visual image search in e-commerce can enhance the shopping experience by making it easier to for customers to find exactly they want.

By using Deep Learning & computer vision you can build this powerful tool that streamlines product discovery.