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Building a retrieval system with lifelogging data is more complicated than with ordinary data due to the redundancies, blurriness, massive amount of data, various sources of information accompanying lifelogging data, and especially the ad-hoc nature of queries. The Lifelog Search Challenge (LSC) is a benchmarking challenge that encourages researchers and developers to push the boundaries in lifelog retrieval. For LSC'22, we develop FIRST 3.0, a novel and flexible system that leverages expressive cross-domain embeddings to enhance the searching process. Our system aims to adaptively capture the semantics of an image at different levels of detail. We also propose to augment our system with an external search engine to help our system with initial visual examples for unfamiliar concepts. Finally, we organize image data in hierarchical clusters based on their visual similarity and location to assist users in data exploration. Experiments show that our system is both fast and effective in handling various retrieval scenarios.

In this paper, we introduce LifeSeeker 4.0 - an interactive lifelog retrieval system developed for the fifth annual Lifelog Search Challenge (LSC'22). In LifeSeeker 4.0, we focus on enhancing our previous system to allow users who have little to no knowledge of underlying system functioning and lifelog data to use it with ease by employing a Contrastive Language-Image Pre-training (CLIP) model. Furthermore, we have exploited the music metadata to facilitate searches that may incorporate emotion. Event clustering is also improved in this version to increase user experience by reducing the occurrence of repeated images, and hence decreasing the search time.

With a huge collection of photos and video clips, it is essential to provide an efficient and easy-to-use system for users to retrieve moments of interest with a wide variation of query types. This motivates us to develop and upgrade our flexible interactive retrieval system for visual lifelog exploration. In this paper, we briefly introduce version 2 of our system with the following main features. Our system supports multiple modalities for interaction and query processing, including visual query by meta-data, text query and visual information matching based on a joint embedding model, scene clustering based on visual and location information, flexible temporal event navigation, and query expansion with visual examples. With the flexibility in system architecture, we expect our system can easily integrate new modules to enhance its functionalities.

In this paper, we present the interactive lifelog retrieval engine developed for the LSC'21 comparative benchmarking challenge. The LifeSeeker 3.0 interactive lifelog retrieval engine is an enhanced version of our previous system participating in LSC'20 - LifeSeeker 2.0. The system is developed by both Dublin City University and the Ho Chi Minh City University of Science. The implementation of LifeSeeker 3.0 focuses on searching and filtering by text query using a weighted Bag-of-Words model with visual concept augmentation and three weighted vocabularies. The visual similarity search is improved using a bag of local convolutional features; while improving the previous version's performance, enhancing query processing time, result displaying, and browsing support.

Lifelog can provide useful insights of our daily activities. It is essential to provide a flexible way for users to retrieve certain events or moments of interest, corresponding to a wide variation of query types. This motivates us to develop FIRST, a Flexible Interactive Retrieval SysTem, to help users to combine or integrate various query components in a flexible manner to handle different query scenarios, such as visual clustering data based on color histogram, visual similarity, GPS location, or scene attributes. We also employ personalized concept detection and image captioning to enhance image understanding from visual lifelog data, and develop an autoencoder-like approach for query text and image feature mapping. Furthermore, we refine the user interface of the retrieval system to better assist users in query expansion and verifying sequential events in a flexible temporal resolution to control the navigation speed through sequences of images.

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.

This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run 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 2020, the 18th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (iii) a coral task about segmenting and labeling collections of coral reef images, and (iv) a new Internet task addressing the problems of identifying hand-drawn user interface components. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 40 groups submitting more than 295 runs.

Lifelogging refers to the process of digitally capturing a con- tinuous 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 chal- lenge 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.

In this paper, the authors described their proposed method in analyzing lifelog data in association with the environment. Tackling the problem of incomplete data, we proposed a replacement method using linear regression method which results in a normalized L2 distance score of 0.0153. Meanwhile, the authors solved the personal air quality subtask by inferring from lifeloggers’ PM2.5 data, which achieves 1.0 in the arithmetic mean of absolute distance score between the predictions and the actual classes.

In this paper we present our interactive lifelog retrieval engine in the LSC'20 comparative benchmarking challenge. The LifeSeeker2.0 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 inter-active 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.

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.