Summarization of LifeLog Data Based on Implicit Feedback from Personal Browsing History

Abstract

LifeLog data is summarized by extracting the data that has most attracted users from vast amounts of information in the LifeLog data. However, because this information is personal and its characteristics vary from person to person, a new technique for estimating individual user's interests is needed. We propose a method that infers the importance of each LifeLog photo based on the user's photo browsing history. We developped a browsing system that displays photos captured with wearable cameras and records the photo browsing history of the user. Using visual techniques such as HSV histograms and bag-of-features in combination with the RankBoost algorithm enabled us to successfully estimate the importance of unknown photos.