My first work with Monet was building a classifier through the use of a convolutional neural network. It was a great introduction to image processing and the power of neural networks.

There was the question of other ways of playing with Monet's works. Since he's a brilliant artist he's been written about frequently. His life and many works have been analyzed by many art historians. Both professional art critics and art lovers alike have lended their voices as well.

With such an interesting corpus it would be interesting to see how NLP can be used to classify his works in various ways. What is the distinct language used to describe his early work to his later. Do people feel more emotion from his city works, or his gardens? There are many possible directions this project could go.

To determine the possibility of whether or not an effective method can be used to cluster Monet's work based around the description of each painting. The final goal is the reation of a 'Monet Curator' that relies on the use of text to create a method of image clustering.

[ ] Gather text data from various Monet Websites. [ ] Interpret key features of text data relative to the images [ ] Map the text from each image and compare to one another. [ ] Start through comparing Early to Later works [ ] Urban versus natural scenes [ ] Color weights