Image Search

In this chapter, we will explore the techniques involved in similarity search with images. We will discuss the evolution of image search over recent years and delve into the mechanism behind image search. As similarity search continues to expand in the world of search today, it is becoming an integral part of what users come to expect.

We will cover the following concepts:

• An overview of image search
• The role of vector search with images
• Creating a vector representation of an image, storing it in Elasticsearch, and performing a kNN search using a source image
• Practical use cases for image and multimedia search today

Overview of image search

Image search is a specialized data retrieval methodology that focuses on finding images through the analysis and comparison of their visual content. The demand for effective image search technology has grown exponentially over the last few years due to an explosion in digital imagery across the internet, social media, and other digital platforms.

The evolution of image search

The origins of image search can be traced back to the early days of the internet when search engines could only analyze text associated with an image, such as filenames, alt text, or surrounding textual content, to match search queries. However, these methods had their limitations, as the actual content of images remained largely ignored.

With advancements in artificial intelligence (AI) and machine learning (ML), the capabilities of image search have greatly expanded. Now, modern image search technology can analyze the actual visual content of images, thanks to the development...

Vector search with images

Vector search is a transformative feature of Elasticsearch and other vector stores that enables a method for performing searches within complex data types such as images. Through this approach, images are converted into vectors that can be indexed, searched, and compared against each other, revolutionizing the way we can retrieve and analyze image data. This inherent characteristic of producing embeddings applies to other media types as well. This section provides an in-depth overview of the vector search process with images, including image vectorization, vector indexing in Elasticsearch, kNN search, vector similarity metrics, and fine-tuning the kNN algorithm.

Image vectorization

The first phase of the vector search process involves transforming the image data into a vector, a process known as image vectorization. Deep learning models, specifically CNNs, are typically employed for this task. CNNs are designed to understand and capture the intricate...

Multi-modal models for vector search

Combining text and image classification allows for a more comprehensive understanding of relationships between multiple types of data. We’ve generated vectors for our images and learned how to search for similar images, so let’s explore what multi-modal vector search is and some practical use cases for it.

Introduction and rationale

The advent of multi-modal models has sparked a seismic shift in our understanding of data analysis and representation. Three examples where this innovative technology can be harnessed include news agencies, e-commerce websites, and social media platforms. Each of these sectors can significantly benefit from the versatility and adaptability of multimedia search, enhancing user experience and boosting their growth.

At their core, multi-modal models seek to integrate and interpret information from different types of data, such as text and images. This integrative approach allows these models to capture...

Summary

In this chapter, our exploration of the world of similarity search with images provided us with an understanding of its evolution and practical workings. We discussed the transformative power of vector-based image search in today’s fast-paced digital environment. We gained the ability to create vector representations of images and integrate them into Elasticsearch. We learned how harnessing the power of kNN search offers a myriad of possibilities for enhancing user experiences. We also saw how the applications of image and multimedia search span across numerous domains, solidifying its importance in the modern digital age.

In the next chapter, we will discuss how NLP models can be used, along with other Elasticsearch features, to redact personally identifiable information before it is ingested into Elasticsearch.