LIFER 2.0: discovering personal lifelog insights using an interactive lifelog retrieval system

This paper describes the participation of the Organiser Team in the ImageCLEFlifelog 2019 Solve My Life Puzzle (Puzzle) and Lifelog Moment Retrieval (LMRT) tasks. We proposed to use LIFER 2.0, an enhanced version of LIFER, which was an interactive retrieval system for personal lifelog data. We utilised LIFER 2.0 with some additional visual features, obtained by using traditional visual bag-of-words, to solve the Puzzle task, while with the LMRT, we applied LIFER 2.0 only with the provided information. The results on both tasks confirmed that by using faceted filter and context browsing, a user can gain insights from their personal lifelog by employing very simple interactions. These results also serve as baselines for other approaches in the ImageCLEFlifelog 2019 challenge to compare with

Exploring Intuitive Lifelog Retrieval and Interaction Modes in Virtual Reality with vitrivr-VR

The multimodal nature of lifelog data collections poses unique challenges for multimedia management and retrieval systems. The Lifelog Search Challenge (LSC) offers an annual evaluation platform for such interactive retrieval systems. They compete against one another in finding items of interest within a set time frame. In this paper, we present the multimedia retrieval system vitrivr-vr, the latest addition to the vitrivr stack, which participated in the LSC in recent years. vitrivr-vr leverages the 3D space in virtual reality (VR) to offer novel retrieval and user interaction models, which we describe with a special focus on design decisions taken for the participation in the LSC

Temporal multimodal video and lifelog retrieval

The past decades have seen exponential growth of both consumption and production of data, with multimedia such as images and videos contributing significantly to said growth. The widespread proliferation of smartphones has provided everyday users with the ability to consume and produce such content easily. As the complexity and diversity of multimedia data has grown, so has the need for more complex retrieval models which address the information needs of users. Finding relevant multimedia content is central in many scenarios, from internet search engines and medical retrieval to querying one's personal multimedia archive, also called lifelog. Traditional retrieval models have often focused on queries targeting small units of retrieval, yet users usually remember temporal context and expect results to include this. However, there is little research into enabling these information needs in interactive multimedia retrieval. In this thesis, we aim to close this research gap by making several contributions to multimedia retrieval with a focus on two scenarios, namely video and lifelog retrieval. We provide a retrieval model for complex information needs with temporal components, including a data model for multimedia retrieval, a query model for complex information needs, and a modular and adaptable query execution model which includes novel algorithms for result fusion. The concepts and models are implemented in vitrivr, an open-source multimodal multimedia retrieval system, which covers all aspects from extraction to query formulation and browsing. vitrivr has proven its usefulness in evaluation campaigns and is now used in two large-scale interdisciplinary research projects. We show the feasibility and effectiveness of our contributions in two ways: firstly, through results from user-centric evaluations which pit different user-system combinations against one another. Secondly, we perform a system-centric evaluation by creating a new dataset for temporal information needs in video and lifelog retrieval with which we quantitatively evaluate our models. The results show significant benefits for systems that enable users to specify more complex information needs with temporal components. Participation in interactive retrieval evaluation campaigns over multiple years provides insight into possible future developments and challenges of such campaigns

Memento: a prototype lifelog search engine for LSC’21

In this paper, we introduce a new lifelog retrieval system called Memento that leverages semantic representations of images and textual queries projected into a common latent space to facilitate effective retrieval. It bridges the semantic gap between complex visual scenes/events and user information needs expressed as textual and faceted queries. The system, developed for the 2021 Lifelog Search Challenge also has a minimalist user interface that includes primary search, temporal search, and visual data filtering components

LifeSeeker 2.0: interactive lifelog search engine at LSC 2020

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

LifeSeeker 3.0 : an interactive lifelog search engine for LSC’21

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

Experiments in lifelog organisation and retrieval at NTCIR

Lifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented

ViRMA: Virtual Reality Multimedia Analytics at LSC 2021

In this paper we describe the first iteration of the ViRMA prototype system, a novel approach to multimedia analysis in virtual reality and inspired by the M3 data model. We intend to evaluate our approach via the Lifelog Search Challenge (LSC) to serve as a benchmark against other multimedia analytics systems

Myscéal 2.0: a revised experimental interactive lifelog retrieval system for LSC'21

Building an interactive retrieval system for lifelogging contains many challenges due to massive multi-modal personal data besides the requirement of accuracy and rapid response for such a tool. The Lifelog Search Challenge (LSC) is the international lifelog retrieval competition that inspires researchers to develop their systems to cope with the challenges and evaluates the effectiveness of their solutions. In this paper, we upgrade our previous Myscéal 2.0 and present Myscéal 2.0 system for the LSC'21 with the improved features inspired by the novice users experiments. The experiments show that a novice user achieved more than half of the expert score on average. To mitigate the gap of them, some potential enhancements were identified and integrated to the enhanced version

Flexible interactive retrieval SysTem 3.0 for visual lifelog exploration at LSC 2022

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