Problem Statement

In our world, text is slowly starting to become less and less important. Most of the content development process is moving towards visuals, and so are the search engines. To enable a search system that can interpret the visuals around a person and drive information from it. The problem statement for this AI-based software development case study was-

“To search similar images for a given image from a dataset.”

Solution Description

The system we developed to address this problem worked on the basis of two major components. These components leverage emerging technologies like deep learning techniques and utilize data storage methods that are known to function efficiently. Here’s a little bit of detail about these elements: 

1. Preprocessing
   • Convert given images into embeddings using the ResNet50 Convolutional Neural Network (CNN) model.
   • Store the extracted embeddings in a vector database, such as Astra DB.
2. Visual Search
   • Convert the given query image into embeddings.
   • Use these embeddings to perform a cosine similarity search against the embeddings stored in the vector database.
   • Recommend images based on the similarity ranking.

Now, let’s understand the technicalities of our system in a detailed solution description. 

Detailed Solution

Preprocessing

Step 1: Image Embedding Extraction Using ResNet50

The first step in our solution is to convert the images into embeddings. Embeddings are dense vector representations of images that capture their essential features. For this, we use ResNet50, a powerful and widely-used deep learning model.

Step 2: Storing Embeddings in a Vector Database

Once we have the embeddings, the next step is to store them in a vector database for efficient similarity search. We use Astra DB, a highly scalable and performant vector database.

Visual Search

Step 1: Query Image Embedding Extraction

To perform a visual search, we first convert the query image into embeddings using the same ResNet50 model as used in the preprocessing step.

Step 2: Cosine Similarity Search

With the query embeddings ready, we perform a cosine similarity search against the embeddings stored in the vector database. 

Step 3: Image Recommendation

Based on the similarity scores, we recommend the top matching images. These images are ranked by their cosine similarity to the query image, ensuring that the most similar images are presented first.

Conclusion

As one of the artificial intelligence development companies, our case study is aligned with the current trends of the market when it comes to information acquisition and interpretation. People have begun to prefer system that function visually, and this case study from TechEnhance caters to the same preference.