If you’ve been keeping track of my various talks & workshops over the last year or so, you will have noticed that I’ve become a little obsessed with analysing images (see power of pixels and/or read more here). As part of my PhD research, I’ve been using images to broaden students’ awareness of what is data, and data science, and it’s been so much fun!
The power of pixels: Using R to explore and exploit features of images
Thursday, Oct 18, 2018, 6:00 PM
G15, Science Building 303, University of Auckland 38 Princes Street Auckland, NZ
30 Members Attending
Kia ora koutou Anna Fergusson, one of our R-ladies Auckland co-organisers, will be the speaker at this meetup. We’ll explore a range of techniques and R packages for working with images, all at an introductory level. Time: 6:00 arrival for a 6:30pm start. What to bring: Laptops with R installed, arrive early if you are a beginner and would like hel…
This is not a teaching-focused workshop, it’s more about learning fun and cool things you can do with images, like making GIFs like the one below….
…. and other cool things, like classifying photos as cats or dogs, or finding the most similar drawing of a duck!
It will be at an introductory level, and you don’t need to be a “lady” to come along, just supportive of gender diversity in the R community (or more broadly, data science)! If you’ve never used R before, don’t worry – just bring yourself along with a laptop and we’ll look after you 🙂
Last night, I saw a tweet announcing that Google had made data available on over 50 million drawings from the game Quick, Draw! I had never played the game before, but it is pretty cool. The idea behind the game is whether a neural network can learn to recognize doodling – watch the video below for more about this (with an example about cats of course!)
For each game, you are challenged to draw a certain object within 20 secs, and you get to see if the algorithm can classify your drawing correctly or not. See my attempt below to draw a trumpet, and the neural network correctly identifying that I was drawing a trumpet.
Since I am clearly obsessed with cats at the moment, I went straight to the drawings of cats. You can see ALL the drawings made for cats (hundred of thousands) and can see variation in particular features of these drawings. I thought it would be cool to be able to take a random sample from all the drawings for a particular category, so after some coding I set up this page: learning.statistics-is-awesome.org/draw/. I’ve included below each drawing the other data provided in the following order:
the word the user was told to draw
the two letter country code
whether the drawing was correctly classified
number of individual strokes made for the drawing
[Update: There are now more variables available – see this post for more details]
So, on average, how many whiskers do Quick, Draw! players draw on their cats?
So, turns out it’s a fairly safe bet that the mean number of whiskers per cat drawing made by Quick, Draw! players is somewhere between 2.2 and 3.5 whiskers. Of course, these are the drawings that have been moderated (I’m assuming for appropriateness/decency). When you look at the drawings, with that 20 second limit on drawing time, you can see that many players went for other features of cats like their ears, possibly running out of time to draw the whiskers. In that respect, it would be interesting to see if there is something going on with whether the drawing was correctly classified as being a cat or not – are whiskers a defining feature of cat drawings?
I reckon there are a tonne of cool things to explore with this dataset, and with the ability to randomly sample from the hundreds and hundreds of thousands of drawings available under each category, a good reason to use statistical inference 🙂 I like that students can develop their own measures based on features of the drawings, based on what they are interested in exploring.
After I published this post, I took a look at the drawings for octopus and then for octagon, a fascinating comparison.
I wonder if players of Quick, Draw! are more likely to draw eight sides for an octagon or eight legs for an octopus? I wonder if the mean number of sides drawn for an octagon is higher than the mean number of legs draw for an octopus?