Overview of imageCLEFlifelog 2019: solve my life puzzle and lifelog Moment retrieval

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

An experiment in Interactive Retrieval for the lifelog moment retrieval task at imageCLEFlifelog2020.

The development of technology has led to an increase in mobile devices’ use to keep track of individual daily activities, as known as Lifelogging. Lifelogging has raised many research challenges, one of which is how to retrieve a specific moment in response to a user’s information need. This paper presents an efficient interactive search engine for large multimodal lifelog data which is evaluated in the ImageCLEFlifelog2020 Lifelog Moment Retrieval task (LMRT). The system is the modified version of the Myscéal demonstrator used in the Lifelog Search Challenge 2020, with the addition of visual similarity and a new method of visualising results. In interactive experimentation, our system achieved an F1@ 10 score of 0.48 in the official submission but can be significantly improved by implementing a number of post-processing steps

Organiser Team at ImageCLEFlifelog 2020: A Baseline Approach for Moment Retrieval and Athlete Performance Prediction using Lifelog Data

For the LMRT task at ImageCLEFlifelog 2020, LIFER 3.0, a new version of the LIFER system with improvements in the user interface and system affordance, is used and evaluated via feedback from a user experiment. In addition, since both tasks share a common dataset, LIFER 3.0 borrows some features from the LifeSeeker system deployed for the Lifelog Search Challenge; which are free-text search, visual similarity search and elastic sequencing filter. For the SPLL task, we proposed a naive solution by capturing the rate of change in running speed and weight, then obtain the target changes for each subtask using average computation and linear regression model. The results presented in this paper can be used as comparative baselines for other participants in the ImageCLEFlifelog 2020 challenge.publishedVersio