Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings

Automatic annotation of X-ray images: a study on attribute selection

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification. of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space

Overview of the ImageCLEFphoto 2008 photographic retrieval task

ImageCLEFphoto 2008 is an ad-hoc photo retrieval task and part of the ImageCLEF evaluation campaign. This task provides both the resources and the framework necessary to perform comparative laboratory-style evaluation of visual information retrieval systems. In 2008, the evaluation task concentrated on promoting diversity within the top 20 results from a multilingual image collection. This new challenge attracted a record number of submissions: a total of 24 participating groups submitting 1,042 system runs. Some of the findings include that the choice of annotation language is almost negligible and the best runs are by combining concept and content-based retrieval methods

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. 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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

Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

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

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

Report of MIRACLE team for the Ad-Hoc track in CLEF 2006

This paper presents the 2006 MIRACLE’s team approach to the AdHoc Information Retrieval track. The experiments for this campaign keep on testing our IR approach. First, a baseline set of runs is obtained, including standard components: stemming, transforming, filtering, entities detection and extracting, and others. Then, a extended set of runs is obtained using several types of combinations of these baseline runs. The improvements introduced for this campaign have been a few ones: we have used an entity recognition and indexing prototype tool into our tokenizing scheme, and we have run more combining experiments for the robust multilingual case than in previous campaigns. However, no significative improvements have been achieved. For the this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, French, Hungarian, and Portuguese. - Bilingual: English to Bulgarian, French, Hungarian, and Portuguese; Spanish to French and Portuguese; and French to Portuguese. - Robust monolingual: German, English, Spanish, French, Italian, and Dutch. - Robust bilingual: English to German, Italian to Spanish, and French to Dutch. - Robust multilingual: English to robust monolingual languages. We still need to work harder to improve some aspects of our processing scheme, being the most important, to our knowledge, the entities recognition and normalization