This is a project which was part of my University of Surrey Comp Sci degree. I applied a siamese CNN model to compare extracted image features of some 7000 images scraped from the British Museum Catalogue.
- To begin we need to scrape our image data by running BMScraper.py and then imageDownloader.py.
- Some images might have a black background, there's enough data to justify removing them but some may be considered valuable, if so run invert.py on the coin group.
- We apply histograms to the image data by running imageHist.py, you may choose to ignore this step or modify the histogram being used.
- Finally the data needs to be converted to a MNIST CSV format, run mnistMaker.py.
- We train two models, feature and simmillarity. Feature compares groups and Simmilarity compares two images, run siamese.py.
- To display the results run loadResults.py
- Charlie Tizzard - Initial work
- Colin O'Keefe for the Web Scraper