Considering Manual Annotations in Dynamic Segmentation of Multimodal Lifelog Data

Multimodal lifelog data consists of continual streams of multimodal sensor data about the life experience of an individual. In order to be effective, any lifelog retrieval system needs to segment continual lifelog data into manageable units. In this paper, we explore the effect of incorporating manual annotations into the lifelog event segmentation process, and we present a study into the effect of high-quality manual annotations on a query-time document segmentation process for lifelog data and evaluate the approach using an open and available test collection. We show that activity based manual annotations enhance the understanding of information retrieval and we highlight a number of potential topics of interest for the community.