Embedding Methods for Image Search

By James Briggs & Laura Carnevali

Learn how to make machines understand images as people do. This free course covers everything you need to build state-of-the-art image retrieval systems; image search, text-to-image, object detection and more.

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Introduction

Image retrieval has a long history, from term-matching manually annotated images in the 70s to today’s state-of-the-art deep learning-based approaches.

In this ebook, we will cover the state-of-the-art methods for image retrieval. We will start with a brief history of the field before diving in to the pillars of image retrieval: similarity search, content-based image retrieval, and multi-modal retrieval.

Image retrieval relies on two components; image embeddings, and vector search. We will cover how to produce information rich image embeddings with state-of-the-art deep learning architectures, including convolutional neural networks and transformers. Following this, we will learn how to pair our image embeddings with vector search to build powerful image retrieval systems.

This ebook is for anyone who wants to build amazing image-search applications using the latest methods in deep learning and information retrieval. No prior knowledge in either is necessary!

Chapter 1

Color Histograms

A look at one of the earliest content-based embedding methods.

Chapter 2

Bag of Visual Words

Content-based information retrieval and classification with visual words.

Chapter 3

Image-net

How ImageNet and AlexNet kickstarted the deep learning era of computer vision.

Chapter 4

Convolutional Neural Nets

A visual tour of the long reigning champions of computer vision.