Introduction

Photographers can spend a lot of time sorting through photos, trying to find the best photo of a particular group, so that everyone is looking, no one is blinking, all heads are visible etc.

My aim is to automate this by the means of computational photography, so that the time spent on this laborious but necessary step is reduced. I aim to detect features within the image that make up good and bad aspects of a photo and classify based on these features.

I myself have spent hours going backwards and forwards through a set of photos, struggling to decide which photos I like the best. Since at the end of the day, these photos may go into a gallery where the people in the photos can buy them, thus they will be more likely to buy the photos if they are given the best selection available.

An extension to this is to integrate face recognition into the gallery, so that the user is only shown photos which they are in.

Furthermore I aim to look at intelligent resizing methods to adjust the composition of images to obey the rule of thirds, automatically correct rotation etc. so that they look more pleasing.

Objectives

• Determine features that can classify good and bad parts of an image
• Collect data set of around 500 images 
• Work out a metric for each feature as to how well it classifies images
• Test on new, unknown images and real world testing
• Refine features from testing
• Create facial recognition gallery
• Investigate intelligent resizing.