I built an image search engine

Introduction

I kind of hate Google image search when stealing images from the internet. I prefer them not to have watermarks from stock photography companies. As a developer, the obvious solution is to build my own image search engine from scratch. Thanks to the magic of neural networks and vector databases, we can easily do that in the next five minutes. In today's article, we'll use JavaScript and a vector database called Weaviate to build a search engine that takes an image as an input and finds images that are similar to it.

Yesterday on the code report, I talked about vector databases, and not everybody was convinced. After this quick project though, you'll be able to see what makes them so powerful and unique.

Keywords

Neural Networks, Vector Databases, Image Search Engine, Weaviate, JavaScript, ResNet 50

FAQ

1. What is the key technology used to build the image search engine? The key technologies used in building the image search engine are neural networks and vector databases, specifically utilizing Weaviate and ResNet 50.

2. How can one store images in the database using the vector database? To store images in the vector database, one needs to convert them to base64 format and use the appropriate schema configuration to define properties like image and text.

3. What advantages do vector databases offer for image search engines? Vector databases allow for objects with similar features, like images that look alike, to be clustered together in a vector space. This enables efficient querying based on proximity and similarity, making image searches more accurate.

4. How does the image search engine handle searching for similar images? The image search engine uses the neural network to create embeddings for images, which automatically updates as more images are added to the database. This allows for easy querying to find images that are most similar to a given input image.