75 research outputs found
Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks
The medical tasks in ImageCLEF have been run every year from 2004-2018 and many different tasks and data sets have been used over these years. The created resources are being used by many researchers well beyond the actual evaluation campaigns and are allowing to compare the performance of many techniques on the same grounds and in a reproducible way. Many of the larger data sets are from the medical literature, as such images are easier to obtain and to share than clinical data, which was used in a few smaller ImageCLEF challenges that are specifically marked with the disease type and anatomic region. This chapter describes the main results of the various tasks over the years, including data, participants, types of tasks evaluated and also the lessons learned in organizing such tasks for the scientific community
ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 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 will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020
Evaluating performance of biomedical image retrieval systems - an overview of the medical image retrieval task at ImageCLEF 2004-2013
Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created
Overview of ImageCLEF 2017: Information extraction from images
This paper presents an overview of the ImageCLEF 2017 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2017. ImageCLEF is an ongoing initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in various usage scenarios and domains. In 2017, the 15th edition of ImageCLEF, three main tasks were proposed and one pilot task: (1) a LifeLog task about searching in LifeLog data, so videos, images and other sources; (2) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based on the figure alone; (3) a tuberculosis task that aims at detecting the tuberculosis type from CT (Computed Tomography) volumes of the lung and also the drug resistance of the tuberculosis; and (4) a remote sensing pilot task that aims at predicting population density based on satellite images. The strong participation of over 150 research groups registering for the four tasks and 27 groups submitting results shows the interest in this benchmarking campaign despite the fact that all four tasks were new and had to create their own community
Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View
Multimedia collections are more than ever growing in size and diversity.
Effective multimedia retrieval systems are thus critical to access these
datasets from the end-user perspective and in a scalable way. We are interested
in repositories of image/text multimedia objects and we study multimodal
information fusion techniques in the context of content based multimedia
information retrieval. We focus on graph based methods which have proven to
provide state-of-the-art performances. We particularly examine two of such
methods : cross-media similarities and random walk based scores. From a
theoretical viewpoint, we propose a unifying graph based framework which
encompasses the two aforementioned approaches. Our proposal allows us to
highlight the core features one should consider when using a graph based
technique for the combination of visual and textual information. We compare
cross-media and random walk based results using three different real-world
datasets. From a practical standpoint, our extended empirical analysis allow us
to provide insights and guidelines about the use of graph based methods for
multimodal information fusion in content based multimedia information
retrieval.Comment: An extended version of the paper: Visual and Textual Information
Fusion in Multimedia Retrieval using Semantic Filtering and Graph based
Methods, by J. Ah-Pine, G. Csurka and S. Clinchant, submitted to ACM
Transactions on Information System
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
Characterizing Health-Related Information Needs of Domain Experts (regular paper)
International audienceIn information retrieval literature, understanding the users' intents behind the queries is critically important to gain a better insight of how to select relevant results. While many studies investigated how users in general carry out exploratory health searches in digital environments, a few focused on how are the queries formulated, specifically by domain expert users. This study intends to fill this gap by studying 173 health expert queries issued from 3 medical information retrieval tasks within 2 different evaluation compaigns. A statistical analysis has been carried out to study both variation and correlation of health-query attributes such as length, clarity and specificity of either clinical or non clinical queries. The knowledge gained from the study has an immediate impact on the design of future health information seeking systems
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien für die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten überprüft.
Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die veränderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt
sich mit der system-basierten Evaluation von Indizierungssystemen für
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen für das Suchen und Verschlagworten von
Bildern, Evaluationsmaße für die Qualitätsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und
experimentell verglichen. Traditionelle Maße zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. Evaluationsmaße vergeben
üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
überprüft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell für die konzeptbasierte Suche von Bildern,
eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die
anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und Qualität der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien für die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten überprüft.
Heutzutage gehören Evaluation, Test und Qualitätsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunächst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme übertragen. Dies geschah häufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die veränderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschäftigt
sich mit der system-basierten Evaluation von Indizierungssystemen für
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen für das Suchen und Verschlagworten von
Bildern, Evaluationsmaße für die Qualitätsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlässigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und Evaluationsmaße zur Qualitätsbewertung eingeführt, analysiert und
experimentell verglichen. Traditionelle Maße zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. Evaluationsmaße vergeben
üblicherweise binäre Kosten für korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
überprüft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ähnlichkeiten visueller Konzepte automatisiert abgeschätzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell für die konzeptbasierte Suche von Bildern,
eine vollständig bewertete Testkollektion und neue Evaluationsmaße für die
anforderungsgerechte Qualitätsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
Use Case Oriented Medical Visual Information Retrieval & System Evaluation
Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation
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