  While wearable cameras are becoming increasingly popular, locating relevant
information in large unstructured collections of egocentric images is still a
tedious and time consuming processes. This paper addresses the problem of
organizing egocentric photo streams acquired by a wearable camera into
semantically meaningful segments. First, contextual and semantic information is
extracted for each image by employing a Convolutional Neural Networks approach.
Later, by integrating language processing, a vocabulary of concepts is defined
in a semantic space. Finally, by exploiting the temporal coherence in photo
streams, images which share contextual and semantic attributes are grouped
together. The resulting temporal segmentation is particularly suited for
further analysis, ranging from activity and event recognition to semantic
indexing and summarization. Experiments over egocentric sets of nearly 17,000
images, show that the proposed approach outperforms state-of-the-art methods.
