de Oliveira-Barra G, Giró-i-Nieto X, Cartas-Ayala A, Radeva P. LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task. In The 12th NTCIR Conference, Evaluation of Information Access Technologies. Tokyo, Japan: National Institute of Informatics (NII); 2016.  (3.22 MB)


Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials.  LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing.  Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.