ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be 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 will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019

Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

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

Overview of ImageCLEFlifelog 2018: daily living understanding and lifelog moment retrieval

Benchmarking in Multimedia and Retrieval related research fields has a long tradition and important position within the community. Benchmarks such as the MediaEval Multimedia Benchmark or CLEF are well established and also served by the community. One major goal of these competitions beside of comparing different methods and approaches is also to create or promote new interesting research directions within multimedia. For example the Medico task at MediaEval with the goal of medical related multimedia analysis. Although lifelogging creates a lot of attention in the community which is shown by several workshops and special session hosted about the topic. Despite of that there exist also some lifelogging related benchmarks. For example the previous edition of the lifelogging task at ImageCLEF. The last years ImageCLEFlifelog task was well received but had some barriers that made it difficult for some researchers to participate (data size, multi modal features, etc.) The ImageCLEFlifelog 2018 tries to overcome these problems and make the task accessible for an even broader audience (eg, pre-extracted features are provided). Furthermore, the task is divided into two subtasks (challenges). The two challenges are lifelog moment retrieval (LMRT) and the Activities of Daily Living understanding (ADLT). All in all seven teams participated with a total number of 41 runs which was an significant increase compared to the previous year

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

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

ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

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

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

Recuperação e identificação de momentos em imagens

In our modern society almost anyone is able to capture moments and record events due to the ease accessibility to smartphones. This leads to the question, if we record so much of our life how can we easily retrieve specific moments? The answer to this question would open the door for a big leap in human life quality. The possibilities are endless, from trivial problems like finding a photo of a birthday cake to being capable of analyzing the progress of mental illnesses in patients or even tracking people with infectious diseases. With so much data being created everyday, the answer to this question becomes more complex. There is no stream lined approach to solve the problem of moment localization in a large dataset of images and investigations into this problem have only started a few years ago. ImageCLEF is one competition where researchers participate and try to achieve new and better results in the task of moment retrieval. This complex problem, along with the interest in participating in the ImageCLEF Lifelog Moment Retrieval Task posed a good challenge for the development of this dissertation. The proposed solution consists in developing a system capable of retriving images automatically according to specified moments described in a corpus of text without any sort of user interaction and using only state-of-the-art image and text processing methods. The developed retrieval system achieves this objective by extracting and categorizing relevant information from text while being able to compute a similarity score with the extracted labels from the image processing stage. In this way, the system is capable of telling if images are related to the specified moment in text and therefore able to retrieve the pictures accordingly. In the ImageCLEF Life Moment Retrieval 2020 subtask the proposed automatic retrieval system achieved a score of 0.03 in the F1-measure@10 evaluation methodology. Even though this scores are not competitve when compared to other teams systems scores, the built system presents a good baseline for future work.Na sociedade moderna, praticamente qualquer pessoa consegue capturar momentos e registar eventos devido à facilidade de acesso a smartphones. Isso leva à questão, se registamos tanto da nossa vida, como podemos facilmente recuperar momentos específicos? A resposta a esta questão abriria a porta para um grande salto na qualidade da vida humana. As possibilidades são infinitas, desde problemas triviais como encontrar a foto de um bolo de aniversário até ser capaz de analisar o progresso de doenças mentais em pacientes ou mesmo rastrear pessoas com doenças infecciosas. Com tantos dados a serem criados todos os dias, a resposta a esta pergunta torna-se mais complexa. Não existe uma abordagem linear para resolver o problema da localização de momentos num grande conjunto de imagens e investigações sobre este problema começaram há apenas poucos anos. O ImageCLEF é uma competição onde investigadores participam e tentam alcançar novos e melhores resultados na tarefa de recuperação de momentos a cada ano. Este problema complexo, em conjunto com o interesse em participar na tarefa ImageCLEF Lifelog Moment Retrieval, apresentam-se como um bom desafio para o desenvolvimento desta dissertação. A solução proposta consiste num sistema capaz de recuperar automaticamente imagens de momentos descritos em formato de texto, sem qualquer tipo de interação de um utilizador, utilizando apenas métodos estado da arte de processamento de imagem e texto. O sistema de recuperação desenvolvido alcança este objetivo através da extração e categorização de informação relevante de texto enquanto calcula um valor de similaridade com os rótulos extraídos durante a fase de processamento de imagem. Dessa forma, o sistema consegue dizer se as imagens estão relacionadas ao momento especificado no texto e, portanto, é capaz de recuperar as imagens de acordo. Na subtarefa ImageCLEF Life Moment Retrieval 2020, o sistema de recuperação automática de imagens proposto alcançou uma pontuação de 0.03 na metodologia de avaliação F1-measure@10. Mesmo que estas pontuações não sejam competitivas quando comparadas às pontuações de outros sistemas de outras equipas, o sistema construído apresenta-se como uma boa base para trabalhos futuros.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

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

Stress detection in lifelog data for improved personalized lifelog retrieval system

Stress can be categorized into acute and chronic types, with acute stress having short-term positive effects in managing hazardous situations, while chronic stress can adversely impact mental health. In a biological context, stress elicits a physiological response indicative of the fight-or-flight mechanism, accompanied by measurable changes in physiological signals such as blood volume pulse (BVP), galvanic skin response (GSR), and skin temperature (TEMP). While clinical-grade devices have traditionally been used to measure these signals, recent advancements in sensor technology enable their capture using consumer-grade wearable devices, providing opportunities for research in acute stress detection. Despite these advancements, there has been limited focus on utilizing low-resolution data obtained from sensor technology for early stress detection and evaluating stress detection models under real-world conditions. Moreover, the potential of physiological signals to infer mental stress information remains largely unexplored in lifelog retrieval systems. This thesis addresses these gaps through empirical investigations and explores the potential of utilizing physiological signals for stress detection and their integration within the state-of-the-art (SOTA) lifelog retrieval system. The main contributions of this thesis are as follows. Firstly, statistical analyses are conducted to investigate the feasibility of using low-resolution data for stress detection and emphasize the superiority of subject-dependent models over subject-independent models, thereby proposing the optimal approach to training stress detection models with low-resolution data. Secondly, longitudinal stress lifelog data is collected to evaluate stress detection models in real-world settings. It is proposed that training lifelog models on physiological signals in real-world settings is crucial to avoid detection inaccuracies caused by differences between laboratory and free-living conditions. Finally, a state-of-the-art lifelog interactive retrieval system called \lifeseeker is developed, incorporating the stress-moment filter function. Experimental results demonstrate that integrating this function improves the overall performance of the system in both interactive and non-interactive modes. In summary, this thesis contributes to the understanding of stress detection applied in real-world settings and showcases the potential of integrating stress information for enhancing personalized lifelog retrieval system performance