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

SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation