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.
Citationde Oliveira Barra, G., Cartas, A., Bolaños, M., Dimiccoli, M., Giro, X., Radeva, P. LEMoRe: A lifelog engine for moments retrieval at the NTCIR-lifelog LSAT task. A: NII Testbeds and Community for Information access Research project. "Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies". Tokyo: 2016.
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