Showing papers by "Duc-Tien Dang-Nguyen published in 2019"
Politehnica University of Bucharest1, University of Applied Sciences Western Switzerland2, University of La Rochelle3, National Institutes of Health4, Philips5, University of Bergen6, University of Cagliari7, University of Oslo8, Ho Chi Minh City University of Science9, Alpen-Adria-Universität Klagenfurt10, Dublin City University11, Dortmund University of Applied Sciences and Arts12, University of Essex13, University of the Aegean14
TL;DR: An overview of the ImageCLEF 2019 lab is presented, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019, and shows an important interest in this benchmark campaign.
Abstract: This paper presents an overview of the ImageCLEF 2019 lab, organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data, (ii) a lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (iii) a new security task addressing the problems of automatically identifying forged content and retrieve hidden information, and (iv) a new coral task about segmenting and labeling collections of coral images for 3D modeling. The strong participation, with 235 research groups registering, and 63 submitting over 359 runs, shows an important interest in this benchmark campaign.
67 citations
08 Jan 2019
TL;DR: The LSC2018 collection, designed to evaluate the performance of interactive retrieval systems, is introduced and the features of the dataset are described and the outcome of the first Lifelog Search Challenge (LSC), which used the dataset in an interactive competition at ACM ICMR 2018.
Abstract: There is a long history of repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists that has been designed to support interactive lifelog retrieval. In this paper we introduce the LSC2018 collection, that is designed to evaluate the performance of interactive retrieval systems. We describe the features of the dataset and we report on the outcome of the first Lifelog Search Challenge (LSC), which used the dataset in an interactive competition at ACM ICMR 2018.
36 citations
Proceedings Article•
09 Sep 2019
TL;DR: In this edition, the task was composed of two subtasks (challenges): the Lifelog Moments Retrieval (LMRT) challenge that followed the same format as in the previous edition, and the Solve My Life Puzzle (Puzzle), a brand new task that focused on rearranging lifelog moments in temporal order.
Abstract: This paper describes ImageCLEFlifelog 2019, the third edition of the Lifelog task. In this edition, the task was composed of two
subtasks (challenges): the Lifelog Moments Retrieval (LMRT) challenge
that followed the same format as in the previous edition, and the Solve
My Life Puzzle (Puzzle), a brand new task that focused on rearranging
lifelog moments in temporal order. ImageCLEFlifelog 2019 received noticeably higher submissions than the previous editions, with ten teams
participating resulting in a total number of 109 runs.
33 citations
TL;DR: The dataset created for this lifelog task, activities of participating teams who took part in these challenges and learnings for the community from the NTCIR-Lifelog challenges are highlighted.
Abstract: Lifelogging refers to the process of digitally capturing a continuous and detailed trace of life activities in a passive manner. In order to assist the research community to make progress in the organisation and retrieval of data from lifelog archives, a lifelog task was organised at NTCIR since edition 12. Lifelog-3 was the third running of the lifelog task (at NTCIR-14) and the Lifelog-3 task explored three different lifelog data access related challenges, the search challenge, the annotation challenge and the insights challenge. In this paper we review the dataset created for this activity, activities of participating teams who took part in these challenges and we highlight learnings for the community from the NTCIR-Lifelog challenges.
6 citations
10 Jun 2019
TL;DR: A new interactive retrieval engine is described that supports faceted retrieval and the results of an initial experiment with four users are presented, followed by a list of changes for a revised interactive retrieval engines for the LSC2019 comparative evaluation competition.
Abstract: This paper describes the work of DCU research team in collaboration with University of Science, Vietnam, and University of Bergen, Norway at the Lifelog task of NTCIR-14. In this paper, a new interactive retrieval engine is described that supports faceted retrieval and we present the results of an initial experiment with four users. Following this initial experiment, we implement a list of changes for a revised interactive retrieval engine for the LSC2019 comparative evaluation competition. The interactive retrieval system we describe utilises the wide range of lifelog metadata provided by the task organisers to develop an extensive faceted retrieval system.
4 citations
13 Sep 2019
TL;DR: This paper proposes a data-driven based approach to solve the first task: replay detection - multi-stream synchronization, which aims to determine the replays which lie between two logo-transitional endpoints and synchronize them with their sources by extracting frames from videos, then applying image processing and retrieval remedies.
Abstract: In GameStory: The 2019 Video Game Analytics Challenge, two main tasks are nominated to solve in the challenge, which are replay detection - multi-stream synchronization, and game story summarization. In this paper, we propose a data-driven based approach to solve the first task: replay detection - multi-stream synchronization. Our solution aims to determine the replays which lie between two logo-transitional endpoints and synchronize them with their sources by extracting frames from videos, then applying image processing and retrieval remedies. In detail, we use the Bag of Visual Words approach to detect the logo-transitional endpoints, which contains multiple replays in between, then employ an Image Signature Matching algorithm for multi-stream synchronization and replay boundaries refinement. The best configuration of our proposed solution manages to achieve the second-highest scores in all evaluation metrics of the challenge.