Adaptive Tag Selection for Image Annotation

Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems

ImageCLEF 2013: The vision, the data and the open challenges

This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. The paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities.This work has been partially supported by the Halser Foundation (B. C.),by the LiMoSINe FP7 project under grant # 288024 (B. T.), by the Khresmoi (grant# 257528) and PROMISE ( grant # 258191) FP 7 projects (H.M.) and by the tranScriptorium FP7 project under grant # 600707 (M. V., R. P.)Caputo ., B.; Muller ., H.; Thomee ., B.; Villegas, M.; Paredes Palacios, R.; Zellhofer ., D.; Goeau ., H.... (2013). ImageCLEF 2013: The vision, the data and the open challenges. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 8138:250-268. https://doi.org/10.1007/978-3-642-40802-1_26S2502688138Muller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: experimental evaluation in visual information retrieval. Springer (2010)Tsikrika, T., Seco de Herrera, A.G., Müller, H.: Assessing the scholarly impact of imageCLEF. In: Forner, P., Gonzalo, J., Kekäläinen, J., Lalmas, M., de Rijke, M. (eds.) CLEF 2011. LNCS, vol. 6941, pp. 95–106. Springer, Heidelberg (2011)Huiskes, M., Lew, M.: The MIR Flickr retrieval evaluation. In: Proceedings of the 10th ACM Conference on Multimedia Information Retrieval, Vancouver, BC, Canada, pp. 39–43 (2008)Huiskes, M., Thomee, B., Lew, M.: New trends and ideas in visual concept detection. In: Proceedings of the 11th ACM Conference on Multimedia Information Retrieval, Philadelphia, PA, USA, pp. 527–536 (2010)Villegas, M., Paredes, R.: Overview of the ImageCLEF 2012 Scalable Web Image Annotation Task. In: CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy (2012)Zellhöfer, D.: Overview of the Personal Photo Retrieval Pilot Task at ImageCLEF 2012. In: CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy (2012)Villegas, M., Paredes, R., Thomee, B.: Overview of the ImageCLEF 2013 Scalable Concept Image Annotation Subtask. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain (2013)Zellhöfer, D.: Overview of the ImageCLEF 2013 Personal Photo Retrieval Subtask. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain (2013)Leafsnap (2011)Plantnet (2013)Mobile flora (2013)Folia (2012)Goëau, H., Bonnet, P., Joly, A., Bakic, V., Boujemaa, N., Barthelemy, D., Molino, J.F.: The imageclef 2013 plant identification task. In: ImageCLEF 2013 Working Notes (2013)Pronobis, A., Xing, L., Caputo, B.: Overview of the CLEF 2009 robot vision track. In: Peters, C., Caputo, B., Gonzalo, J., Jones, G.J.F., Kalpathy-Cramer, J., Müller, H., Tsikrika, T. (eds.) CLEF 2009. LNCS, vol. 6242, pp. 110–119. Springer, Heidelberg (2010)Pronobis, A., Caputo, B.: The robot vision task. In: Muller, H., Clough, P., Deselaers, T., Caputo, B. (eds.) ImageCLEF. The Information Retrieval Series, vol. 32, pp. 185–198. Springer, Heidelberg (2010)Pronobis, A., Christensen, H.I., Caputo, B.: Overview of the imageCLEF@ICPR 2010 robot vision track. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 171–179. Springer, Heidelberg (2010)Martinez-Gomez, J., Garcia-Varea, I., Caputo, B.: Overview of the imageclef 2012 robot vision task. In: CLEF 2012 Working Notes (2012)Rusu, R., Cousins, S.: 3d is here: Point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: International Conference on Computer Vision, pp. 1–8. Citeseer (2007)Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Linde, O., Lindeberg, T.: Object recognition using composed receptive field histograms of higher dimensionality. In: Proc. ICPR. Citeseer (2004)Orabona, F., Castellini, C., Caputo, B., Luo, J., Sandini, G.: Indoor place recognition using online independent support vector machines. In: Proc. BMVC, vol. 7 (2007)Orabona, F., Castellini, C., Caputo, B., Jie, L., Sandini, G.: On-line independent support vector machines. Pattern Recognition 43, 1402–1412 (2010)Orabona, F., Jie, L., Caputo, B.: Online-Batch Strongly Convex Multi Kernel Learning. In: Proc. of Computer Vision and Pattern Recognition, CVPR (2010)Orabona, F., Jie, L., Caputo, B.: Multi kernel learning with online-batch optimization. Journal of Machine Learning Research 13, 165–191 (2012)Clough, P., Müller, H., Sanderson, M.: The CLEF 2004 cross-language image retrieval track. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 597–613. Springer, Heidelberg (2005)Clough, P., Müller, H., Deselaers, T., Grubinger, M., Lehmann, T.M., Jensen, J., Hersh, W.: The CLEF 2005 cross–language image retrieval track. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 535–557. Springer, Heidelberg (2006)Müller, H., Deselaers, T., Deserno, T., Clough, P., Kim, E., Hersh, W.: Overview of the imageCLEFmed 2006 medical retrieval and medical annotation tasks. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 595–608. Springer, Heidelberg (2007)Müller, H., Deselaers, T., Deserno, T., Kalpathy–Cramer, J., Kim, E., Hersh, W.: Overview of the imageCLEFmed 2007 medical retrieval and medical annotation tasks. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 472–491. Springer, Heidelberg (2008)Müller, H., Kalpathy–Cramer, J., Eggel, I., Bedrick, S., Radhouani, S., Bakke, B., Kahn Jr., C.E., Hersh, W.: Overview of the CLEF 2009 medical image retrieval track. In: Peters, C., Caputo, B., Gonzalo, J., Jones, G.J.F., Kalpathy-Cramer, J., Müller, H., Tsikrika, T. (eds.) CLEF 2009, Part II. LNCS, vol. 6242, pp. 72–84. Springer, Heidelberg (2010)Tommasi, T., Caputo, B., Welter, P., Güld, M.O., Deserno, T.M.: Overview of the CLEF 2009 medical image annotation track. In: Peters, C., Caputo, B., Gonzalo, J., Jones, G.J.F., Kalpathy-Cramer, J., Müller, H., Tsikrika, T. (eds.) CLEF 2009. LNCS, vol. 6242, pp. 85–93. Springer, Heidelberg (2010)Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds.): ImageCLEF – Experimental Evaluation in Visual Information Retrieval. The Springer International Series on Information Retrieval, vol. 32. Springer, Heidelberg (2010)Kalpathy-Cramer, J., Müller, H., Bedrick, S., Eggel, I., García Seco de Herrera, A., Tsikrika, T.: The CLEF 2011 medical image retrieval and classification tasks. In: Working Notes of CLEF 2011 (Cross Language Evaluation Forum) (2011)Müller, H., García Seco de Herrera, A., Kalpathy-Cramer, J., Demner Fushman, D., Antani, S., Eggel, I.: Overview of the ImageCLEF 2012 medical image retrieval and classification tasks. In: Working Notes of CLEF 2012 (Cross Language Evaluation Forum) (2012)García Seco de Herrera, A., Kalpathy-Cramer, J., Demner Fushman, D., Antani, S., Müller, H.: Overview of the ImageCLEF 2013 medical tasks. In: Working Notes of CLEF 2013 (Cross Language Evaluation Forum) (2013

Automatic tagging and geotagging in video collections and communities

Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

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

ImageCLEF 2014: Overview and analysis of the results

This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medical archives. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the image annotation and retrieval research landscape. The 2014 edition consists of four tasks: domain adaptation, scalable concept image annotation, liver CT image annotation and robot vision. This paper describes the tasks and the 2014 competition, giving a unifying perspective of the present activities of the lab while discussing future challenges and opportunities.This work has been partially supported by the tranScriptorium FP7 project under grant #600707 (M. V., R. P.).Caputo, B.; Müller, H.; Martinez-Gomez, J.; Villegas Santamaría, M.; Acar, B.; Patricia, N.; Marvasti, N.... (2014). ImageCLEF 2014: Overview and analysis of the results. En Information Access Evaluation. Multilinguality, Multimodality, and Interaction: 5th International Conference of the CLEF Initiative, CLEF 2014, Sheffield, UK, September 15-18, 2014. Proceedings. Springer Verlag (Germany). 192-211. https://doi.org/10.1007/978-3-319-11382-1_18S192211Bosch, A., Zisserman, A.: Image classification using random forests and ferns. In: Proc. CVPR (2007)Caputo, B., Müller, H., Martinez-Gomez, J., Villegas, M., Acar, B., Patricia, N., Marvasti, N., Üsküdarlı, S., Paredes, R., Cazorla, M., Garcia-Varea, I., Morell, V.: ImageCLEF 2014: Overview and analysis of the results. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, Springer, Heidelberg (2014)Caputo, B., Patricia, N.: Overview of the ImageCLEF 2014 Domain Adaptation Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014)de Carvalho Gomes, R., Correia Ribas, L., Antnio de Castro Jr., A., Nunes Gonalves, W.: CPPP/UFMS at ImageCLEF 2014: Robot Vision Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014)Del Frate, F., Pacifici, F., Schiavon, G., Solimini, C.: Use of neural networks for automatic classification from high-resolution images. IEEE Transactions on Geoscience and Remote Sensing 45(4), 800–809 (2007)Feng, S.L., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II–1002. IEEE (2004)Friedl, M.A., Brodley, C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 61(3), 399–409 (1997)Goh, K.-S., Chang, E.Y., Li, B.: Using one-class and two-class svms for multiclass image annotation. IEEE Transactions on Knowledge and Data Engineering 17(10), 1333–1346 (2005)Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: Proc. CVPR. Extended Version Considering its Additional MaterialJie, L., Tommasi, T., Caputo, B.: Multiclass transfer learning from unconstrained priors. In: Proc. ICCV (2011)Kim, S., Park, S., Kim, M.: Image classification into object / non-object classes. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 393–400. Springer, Heidelberg (2004)Ko, B.C., Lee, J., Nam, J.Y.: Automatic medical image annotation and keyword-based image retrieval using relevance feedback. Journal of Digital Imaging 25(4), 454–465 (2012)Kökciyan, N., Türkay, R., Üsküdarlı, S., Yolum, P., Bakır, B., Acar, B.: Semantic Description of Liver CT Images: An Ontological Approach. IEEE Journal of Biomedical and Health Informatics (2014)Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.  2, pp. 2169–2178. IEEE (2006)Martinez-Gomez, J., Garcia-Varea, I., Caputo, B.: Overview of the imageclef 2012 robot vision task. In: CLEF (Online Working Notes/Labs/Workshop) (2012)Martinez-Gomez, J., Garcia-Varea, I., Cazorla, M., Caputo, B.: Overview of the imageclef 2013 robot vision task. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes (2013)Martinez-Gomez, J., Cazorla, M., Garcia-Varea, I., Morell, V.: Overview of the ImageCLEF 2014 Robot Vision Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014)Mueen, A., Zainuddin, R., Baba, M.S.: Automatic multilevel medical image annotation and retrieval. Journal of Digital Imaging 21(3), 290–295 (2008)Muller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: experimental evaluation in visual information retrieval. Springer (2010)Park, S.B., Lee, J.W., Kim, S.K.: Content-based image classification using a neural network. Pattern Recognition Letters 25(3), 287–300 (2004)Patricia, N., Caputo, B.: Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: Proc. CVPR (2014)Pronobis, A., Caputo, B.: The robot vision task. In: Muller, H., Clough, P., Deselaers, T., Caputo, B. (eds.) ImageCLEF. The Information Retrieval Series, vol. 32, pp. 185–198. Springer, Heidelberg (2010)Pronobis, A., Christensen, H., Caputo, B.: Overview of the imageclef@ icpr 2010 robot vision track. In: Recognizing Patterns in Signals, Speech, Images and Videos, pp. 171–179 (2010)Qi, X., Han, Y.: Incorporating multiple svms for automatic image annotation. Pattern Recognition 40(2), 728–741 (2007)Reshma, I.A., Ullah, M.Z., Aono, M.: KDEVIR at ImageCLEF 2014 Scalable Concept Image Annotation Task: Ontology based Automatic Image Annotation. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes. Sheffield, UK, September 15-18 (2014)Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)Sahbi, H.: CNRS - TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain, September 23-26 (2013)Sethi, I.K., Coman, I.L., Stan, D.: Mining association rules between low-level image features and high-level concepts. In: Aerospace/Defense Sensing, Simulation, and Controls, pp. 279–290. 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The scholarly impact of TRECVid (2003-2009)

This paper reports on an investigation into the scholarly impact of the TRECVid (TREC Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis a vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams ‘scored’ highly against the evaluation criteria and the relationship between ‘top scoring’ teams at TRECVid and the ‘top scoring’ papers in terms of citations is analysed. A strong relationship was found between ‘success’ at TRECVid and ‘success’ at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed

Image Labeling on a Network: Using Social-Network Metadata for Image Classification

Large-scale image retrieval benchmarks invariably consist of images from the Web. Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community. Such communities generate rich metadata that can naturally be harnessed for image classification and retrieval. Here we study four popular benchmark datasets, extending them with social-network metadata, such as the groups to which each image belongs, the comment thread associated with the image, who uploaded it, their location, and their network of friends. Since these types of data are inherently relational, we propose a model that explicitly accounts for the interdependencies between images sharing common properties. We model the task as a binary labeling problem on a network, and use structured learning techniques to learn model parameters. We find that social-network metadata are useful in a variety of classification tasks, in many cases outperforming methods based on image content.Comment: ECCV 2012; 14 pages, 4 figure

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

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