Metric Based Automatic Event Segmentation and Network Properties of Experience Graphs

Lifelogging, as a growing interest, is a term referring to people digitally capturing all the information produced by them in daily life. Lifelog is a data collection of records of an individual’s daily activities in one or more media forms. In this thesis, we collect lifelog data by using a mobile phone or a Microsoft Research SenseCam worn around subjects’ necks during their daily life. We then propose a way to organize the lifelog data a metricbased model for event segmentation. Further more, we analyse the data properties through constructing the experience graphs from the recorded images. This thesis involves two parts, the details are as follows: First we describe a metric-based model for event segmentation of sensor data recorded by a mobile phone worn around subjects’ necks during their daily life. More specifically, we aim at detecting human daily event boundaries by analysing the recorded triaxial accelerometer signals and images sequence (lifelog data). In the experiments, different signal representations and three boundary detection models are evaluated on a corpus of 2 subjects over total 24 days. The contribution of this work is three-fold. First, we find that using accelerometer signals can provide much more reliable and significantly better performance than using image signals with MPEG-7 low level features. Second, the models using the accelerometer data based on the world’s coordinates system can provide equally or even much better performance than using the accelerometer data based on the device’s