Meme Vision: the science of classifying memes

Meme Vision: the science of classifying memes

Image recognition does not always require neural nets; efficiency and accuracy are achievable with simpler models. As a person of culture and science, I decided to build a model to identify memes.

As a person of culture and science, I decided to build a model to identify memes. This problem is far simpler than the Image-Net competition and so a simpler solution is appropriate. I will demonstrate this by comparing the “Meme Vision” framework to ResNet-50 (the winner of Image-Net 2015).

Method: Meme Vision framework

In a previous article I explained the radial histogram method;

Radial Color Histograms

When color, composition and compute all matter for your computer vision problem — radially reduce the representation…

towardsdatascience.com

(TL;DR — it measures the distribution of color in each segment of the image)

Below we see how this can reduce images to very low dimensional representations.

Image for post

Basic radial color histogram example with 3 bins per color channel and 4 segments (giving 4*3³=108 features)

The final Meme Vision model uses a few extra steps:

  • Convert from RGB to HSV - color degradation is less of a problem to computers when viewed in the HSV palette.
  • Log transformation of pixel counts to help focus on the little differences.
  • Use 8 bins per channel (instead of 3) to distinguish similar color shades, which results 2048 features (instead of 108).
  • Feed these features into a linear support vector machine.

image-recognition optimisation neural-networks memes image-classifier neural networks

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