Egocentric Image Retrieval with Convolutional Neural Networks

Abstract

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.