  Segmenting video content into events provides semantic structures for
indexing, retrieval, and summarization. Since motion cues are not available in
continuous photo-streams, and annotations in lifelogging are scarce and costly,
the frames are usually clustered into events by comparing the visual features
between them in an unsupervised way. However, such methodologies are
ineffective to deal with heterogeneous events, e.g. taking a walk, and
temporary changes in the sight direction, e.g. at a meeting. To address these
limitations, we propose Contextual Event Segmentation (CES), a novel
segmentation paradigm that uses an LSTM-based generative network to model the
photo-stream sequences, predict their visual context, and track their
evolution. CES decides whether a frame is an event boundary by comparing the
visual context generated from the frames in the past, to the visual context
predicted from the future. We implemented CES on a new and massive lifelogging
dataset consisting of more than 1.5 million images spanning over 1,723 days.
Experiments on the popular EDUB-Seg dataset show that our model outperforms the
state-of-the-art by over 16% in f-measure. Furthermore, CES' performance is
only 3 points below that of human annotators.
