📷 Image classifier with Dynamic Time Warping

Image classifier for ppm images.

To find the nearest neighbour for the query, the tool implements a bounded priority queue processing the given images from the training data set. The nearest neighbour class is the guess for the query.

Project carried out in the context of the Data structures and Algorithm course "INFO0902" given by Pr. Geurts, University of Liège.

Grade : 19/20

To determine the best fit for the query, the algorithm used is Dynamic time warping

The training and test sets come from the Google QuickDraw dataset.

Summary

1. Try it!
2. Data sets format
3. Credits

Try it!

Compile the tool

make

Classify one image

To classify one image from testset.txt, run :

./NNSketch -i index -k k -o ./results trainingset.txt testset.txt

Where :

• index is the index of the image in testset.txt.
• k is the number of images from trainingset.txt to use for the classification.

Example :

./NNSketch -i 1 -k 200 -o ./results trainingset.txt testset.txt

Note : the image selected to be classified is visible in ./results

Evaluate the DTW performances

To test ²the algorithm on all the test set, run :

./NNSketch trainingset.txt testset.txt

Data sets format

The two data sets have the same structure :

number of images

first image

• class
• number of pixels
• pixels

...

last image

Credits

• Simon Gardier (Co-author)
• Lei Yang (Co-author)