Recent advances in lifelogging technologies, and in particular, in the field of wearable cameras, have made possible to capture continuously our daily life from a first-person point of view and in a free-hand fashion. However, given the huge amount of images captured and the rate to which they increase (up to 2000 images per day), there is a strong need for efficient and scalable indexing and retrieval systems over egocentric images. To cope with those requirements, we develop a full Content-Based Image Retrieval system based on Convolutional Neural Network (CNN) features. We use egocentric images to create a Lucene index with off-the-shelf features extracted from a pre-trained CNN. Finally, we provide a web-based prototype for egocentric image search and retrieval and tested its performances on the EDUB egocentric dataset.
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