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).
In a previous article I explained the radial histogram method;
(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.
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:
During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images…
My latest project at Flatiron was to use neural networks to classify satellite image tiles. I chose to use a convolutional neural network (CNN).
Board Game Image Recognition using Neural Networks. How to use computer vision techniques to identify chess pieces and their location on a chessboard
It is instructive for instance to trace the computer industry’s to decline in vision, idealism, creativity, romance and sheer fun as it becomes more important and prosperous. Let's look into computational neural network architecture and constructing a cnn model for detection of ship using satellite imagery.
Neural networks, as their name implies, are computer algorithms modeled after networks of neurons in the human brain. Learn more about neural networks from Algorithmia.