The Lifelog Search Challenge (LSC) is an international content retrieval competition that evaluates search for personal lifelog data. At the LSC, content-based search is performed over a multi-modal dataset, continuously recorded by a lifelogger over 27 days, consisting of multimedia content, biometric data, human activity data, and information activities data. In this work, we report on the first LSC that took place in Yokohama, Japan in 2018 as a special workshop at ACM International Conference on Multimedia Retrieval 2018 (ICMR 2018). We describe the general idea of this challenge, summarise the participating search systems as well as the evaluation procedure, and analyse the search performance of the teams in various aspects. We try to identify reasons why some systems performed better than others and provide an outlook as well as open issues for upcoming iterations of the challenge.
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.
This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a LifeLog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on visual question answering where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign.
For the fifth time since 2018, the Lifelog Search Challenge (LSC) facilitated a benchmarking exercise to compare interactive search systems designed for multimodal lifelogs. LSC'22 attracted nine participating research groups who developed interactive lifelog retrieval systems enabling fast and effective access to lifelogs. The systems competed in front of a hybrid audience at the LSC workshop at ACM ICMR'22. This paper presents an introduction to the LSC workshop, the new (larger) dataset used in the competition, and introduces the participating lifelog search systems.
In this paper we present our interactive lifelog retrieval engine in the LSC'20 comparative benchmarking challenge. The LifeSeeker 2.0 interactive lifelog retrieval engine is developed by both Dublin City University and Ho Chi Minh University of Science, which represents an enhanced version of the two corresponding interactive lifelog retrieval engines in LSC'19. The implementation of LifeSeeker 2.0 has been designed to focus on the searching by text query using a Bag-of-Words model with visual concept augmentation and additional improvements in query processing time, enhanced result display and browsing support, and interacting with visual graphs for both query and filter purposes. CCS CONCEPTS • Information systems → Multimedia databases; Users and interactive retrieval; Search interfaces; • Human-centered computing → Interactive systems and tools.
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