The concept of vector embeddings across images is similar but with some key differences in the process compared to words. 

Here's how it works for images:

1. Training on Image Data: Similar to words, a machine learning model, often a Convolutional Neural Network (CNN), is trained on a massive dataset of images. This dataset could contain millions of images with varying content.

2. Learning Visual Features: Instead of meaning and relationships, the model learns to identify visual features from the images. These features can include things like shapes, colors, textures, edges, and spatial relationships between objects.

3. Image to Vector: Once trained, the model can then process a new image and generate a vector representation for it. This vector, like with words, is a multi-dimensional array of numbers capturing the essence of the image in a lower-dimensional space.

4. Visually Similar Images Cluster: Similar to words, images with similar visual content will have vectors closer together in the high-dimensional space. So, an image of a cat would likely have a vector close to other images of cats, even if the poses or backgrounds differ.

Key Differences:

• Focus: While word embeddings focus on meaning and relationships, image embeddings focus on capturing the visual content of the image.

• Input Data: The training data for image embeddings is visual data (images) compared to textual data (words) for word embeddings.

• Model Architecture: CNNs are typically used for image embeddings due to their effectiveness in capturing spatial relationships in images.

Overall, vector embeddings provide a powerful way to represent both text and images in a way that machines can understand and process. This allows for various applications in computer vision tasks like image search, recommendation systems, and object recognition.

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