@conference {cdeOliveira-Barraa, title = {Large Scale Content-Based Video Retrieval with LIvRE}, booktitle = {14th International Workshop on Content-based Multimedia Indexing (CBMI)}, year = {2016}, month = {06/2016}, publisher = {IEEE}, organization = {IEEE}, address = {Bucharest, Romania}, abstract = {

The fast growth of video data requires robust, efficient, and scalable systems to allow for indexing and retrieval. These systems must be accessible from lightweight, portable and usable interfaces to help users in management and search of video content. This demo paper presents LIvRE, an extension of an existing open source tool for image retrieval to support video indexing. LIvRE consists of three main system components (pre-processing, indexing and retrieval), as well as a scalable and responsive HTML5 user interface accessible from a web browser. LIvRE supports image-based queries, which are efficiently matched with the extracted frames of the indexed videos.

}, author = {Gabriel de Oliveira-Barra and Lux, Mathias and Xavier Gir{\'o}-i-Nieto} } @conference {cdeOliveira-Barra, title = {LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task}, booktitle = {The 12th NTCIR Conference, Evaluation of Information Access Technologies}, year = {2016}, month = {06/2016}, publisher = {National Institute of Informatics (NII)}, organization = {National Institute of Informatics (NII)}, address = {Tokyo, Japan}, abstract = {

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.

LEMoRe - A Lifelog Engine for Moments Retrieval at NTCIR-12 from University of Barcelona
}, url = {http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/NTCIR/toc_ntcir.html$\#$Lifelog}, author = {Gabriel de Oliveira-Barra and Xavier Gir{\'o}-i-Nieto and Cartas-Ayala, Alejandro and Radeva, Petia} } @mastersthesis {xBarra, title = {LIvRE: A Video Extension to the LIRE Content-Based Image Retrieval System}, year = {2015}, abstract = {

Advisors:\ Mathias Lux\ (Klagenfurt University) and\ Xavier Gir{\'o}-i-Nieto\ (UPC)

Degree: Electronic Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

Grade: A (9.0/10.0)

This project explores the expansion of Lucene Image Retrieval Engine (LIRE), an open-source Content-Based Image Retrieval (CBIR) system, for video retrieval on large scale video datasets. The fast growth of the need to store huge amounts of video in servers requires efficient, scalable search and indexing engines capable to assist users in their management and retrieval. In our tool, queries are formulated by visual examples allowing users to find the videos and the moment of time when the query image is matched with. The video dataset used on this scenario comprise over 1,000 hours of different news broadcast channels. This thesis presents an extension and adaptation of Lire and its plugin for Solr, an open-source enterprise search platform from the Apache Lucene project, for video retrieval based on visual features, as well as a web-interface for users from different devices.

LIvRE: A Video Extension to the LIRE Content-Based Image Retrieval System from Xavier Giro
}, url = {http://upcommons.upc.edu/handle/2117/79052}, author = {Gabriel de Oliveira-Barra}, editor = {Xavier Gir{\'o}-i-Nieto and Lux, Mathias} }