The SenseCam is a passively capturing wearable camera that captures approximately 2,500 images on average per day. This provides a user with an extensive visual diary. Possible applications making use of this device include helping dementia sufferers recall events from short-term memory, and also this device can be used by tourists to maintain an extensive image collection of particular trips. However a large image collection will quickly build up, with an average of 1 million images captured each year. This presents a considerable challenge in terms of managing such a large collection and to make it accessible for users. My research proposes to address this problem in 4 steps: 1) Identifying distinct events within the 2,500 images that are captured daily 2) Highlighting the most unique of those events 3) Finding similar events to a given event 4) Augmenting the low-quality images from the wearable camera with higher quality images from external sources.