Embedding Methods for Image Search

James Briggs
Laura Carnevali
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.

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!

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Chapter 01

Color Histograms

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

Chapter 02

Bag of Visual Words

Content-based information retrieval and classification with visual words.

Chapter 03

Image-net

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

Chapter 04

Convolutional Neural Nets

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

Chapter 05

Vision Transformers

A deep dive into the unification of NLP and computer vision with the Vision Transformer (ViT).

Chapter 06

CLIP Explained

Multi-modality and the future of computer vision with OpenAI's CLIP.

Chapter 07

Zero-shot Image Classification with OpenAI's CLIP

A deep dive on OpenAI's CLIP for zero-shot image classification.

Chapter 08

Object Localization and Detection with CLIP

How to apply CLIP to object detection in a zero-shot setting.

Chapter 09

Traditional Image Embeddings Methods

An overview of the pre-DL methods for image embedding.

Chapter 10

Extracting meaning with convolutional neural nets (CNNs).

Chapter 11

Diffusion Explained

A deep dive on diffuser models

Chapter 12

And more...

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