A Deep Convolutional Denoising Autoencoder for Image Classification

Left: Photo taken by user. Right: Card predicted by the system.


The Vivino iOS app
More than 36,000 different Magic cards have been released to this date.

Existing Systems

Existing Systems Explained: Perceptual Hashing

Existing Systems Explained: Rectangle Detection

Left: User photo on a black wooden table. The user has been sloppy when taking the photo — the card is rotated, there’s a lot of reflection and someone has written the letters MP on the sleeve. Right: The corresponding reference image.
Left: User photo with rectangle detection applied. It now more closely resembles the reference image but upper left corner is still showing reflections, lower left corner is darker from the shadow cast by the user’s hand and the text writen on the sleeve is still visible. Right: The corresponding reference image.

Alternative to Existing Systems: Deep Learning


Standard Image Classification With Deep Neural Networks?

Transfer Learning?

The Solution: Fingerprinting With Neural Networks

The same card, Elvish Archdruid, printed in three different sets.


The basic architecture of an autoencoder.

Cosine Distance

The cosine distance measures the angle between two vectors. A small angle means a small distance.

Denoising Autoencoders

The basic architecture of my denoising autoencoder.
The evolution of the autoencoder as it is being trained. Top: Training images. Bottom: Output from the autoencoder.

Are these really autoencoders?

The Data

Left: Reference image. Right: Reference image augmented to simulate a user photo.
Cards lined up, ready to shoot photos!

A Couple of Notes on the Test Set



Future Work

Further Reading

Bonus Info

Left: Photo of Chinese card. Right: English card predicted by the system.

Søren L Kristiansen

Written by

Developer, data scientist and Philosopher. Co-founder of Guts & Glory, creator of Readery and 🤘Lemmy.

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