Embeddings & Feature Vectors

Turn images and videos into numbers that machines can understand. Embeddings and feature vectors are the backbone of computer vision and AI-powered applications.

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Table of contents

• What are embeddings and feature vectors?
• How do embeddings work?
• Why are embeddings critical for computer vision?
• Use cases for embeddings and feature vectors
• Embeddings in BaaS pipelines
• FAQs

What are embeddings and feature vectors?

In computer vision, raw pixels aren’t enough. Machines need a structured way to represent the meaning of an image or video. That’s where embeddings and feature vectors come in.

• A feature vector is a numeric representation of data — an array of numbers that capture patterns, textures, shapes, or semantic meaning.
• An embedding is the process (and result) of mapping raw data into this vector space using a model.

Think of embeddings as a translation layer: they turn messy visual inputs into machine-readable numbers.

How do embeddings work?

When an image or frame is passed through a model (like a CNN, transformer, or autoencoder), the model extracts features and condenses them into a vector.

• Each number in the vector corresponds to a learned feature.
• Similar images map to vectors that are close together in high-dimensional space.
• Dissimilar images map far apart.

This makes embeddings powerful for search, clustering, and comparison.

For example:

• A photo of a basketball and a soccer ball will produce vectors close to each other.
• A basketball photo and a cat photo will produce vectors far apart.

Why are embeddings critical for computer vision?

Embeddings are the foundation of:

• Similarity search — finding “things that look like this.”
• Classification — grouping images into categories.
• Clustering — organizing massive datasets into meaningful groups.
• Recommendation engines — surfacing related items.
• Generative AI grounding — giving LLMs visual context via retrieval-augmented generation (RAG).

Without embeddings, vision systems would be limited to raw pixels, which carry no semantic meaning.

Use cases for embeddings and feature vectors

• E-commerce: “Find similar products” search.
• Healthcare: Comparing patient scans for early diagnosis.
• Security: Face recognition and anomaly detection.
• Media: Content moderation and copyright detection.
• Industrial AI: Defect clustering, part matching, predictive maintenance.

Embeddings in BaaS pipelines

With a BaaS platform like Lid Vizion, embeddings become a native building block:

• Automatically generate embeddings for each uploaded image or video.
• Store them alongside metadata in a database.
• Perform fast similarity search with vector indexes.
• Trigger downstream workflows when embeddings match a threshold.

This removes the complexity of managing your own vector infrastructure while keeping embeddings tightly integrated with the rest of your computer vision pipeline.

FAQs

What’s the difference between a feature vector and an embedding?
A feature vector is the numeric output itself; an embedding is the representation plus the process that creates it.

Do embeddings only apply to images?
No. Text, audio, and video can all be embedded. Multimodal embeddings combine them.

How large are embeddings?
It depends on the model. Some embeddings are 128 dimensions, others 1,024 or more. Larger embeddings capture more nuance but require more compute.

Are embeddings static or dynamic?
They can be both. Some models generate fixed embeddings, while others fine-tune embeddings for specific tasks.

How are embeddings stored?
Typically in a vector database, which allows fast similarity search at scale.

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