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. International Society for Optics and Photonics (2001)Shi, R., Feng, H., Chua, T.-S., Lee, C.-H.: An adaptive image content representation and segmentation approach to automatic image annotation. 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. 545–554. Springer, Heidelberg (2004)Tommasi, T., Caputo, B.: Frustratingly easy nbnn domain adaptation. In: Proc. ICCV (2013)Tommasi, T., Quadrianto, N., Caputo, B., Lampert, C.H.: Beyond dataset bias: Multi-task unaligned shared knowledge transfer. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 1–15. Springer, Heidelberg (2013)Tsikrika, T., de Herrera, A.G.S., 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)Ünay, D., Soldea, O., Akyüz, S., Çetin, M., Erçil, A.: Medical image retrieval and automatic annotation: Vpa-sabanci at imageclef 2009. In: The Cross-Language Evaluation Forum (CLEF) (2009)Vailaya, A., Figueiredo, M.A., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10(1), 117–130 (2001)Villegas, M., Paredes, R.: Overview of the ImageCLEF 2012 Scalable Web Image Annotation Task. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy, September 17-20 (2012), http://mvillegas.info/pub/Villegas12_CLEF_Annotation-Overview.pdfVillegas, M., Paredes, R.: Overview of the ImageCLEF 2014 Scalable Concept Image Annotation Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes, Sheffield, UK, September 15-18 (2014), http://mvillegas.info/pub/Villegas14_CLEF_Annotation-Overview.pdfVillegas, 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, September 23-26 (2013), http://mvillegas.info/pub/Villegas13_CLEF_Annotation-Overview.pdfVillena Román, J., González Cristóbal, J.C., Goñi Menoyo, J.M., Martínez Fernández, J.L.: MIRACLE’s naive approach to medical images annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088–1099 (2005)Wong, R.C., Leung, C.H.: Automatic semantic annotation of real-world web images. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1933–1944 (2008)Yang, C., Dong, M., Fotouhi, F.: Image content annotation using bayesian framework and complement components analysis. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 1, pp. I–1193. IEEE (2005)Yılmaz, K.Y., Cemgil, A.T., Simsekli, U.: Generalised coupled tensor factorisation. In: Advances in Neural Information Processing Systems, pp. 2151–2159 (2011)Zhang, Y., Qin, J., Chen, F., Hu, D.: NUDTs Participation in ImageCLEF Robot Vision Challenge 2014. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014

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

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.This work was supported by the Ministerio de Economia y Competitividad of the Spanish Government, and by Consejería de Educación, Cultura y Deportes of the JCCM regional government through project PPII-2014-015-P (grant number DPI2013-40534-R). Jesus Martínez-Gómez is also funded by the JCCM (grant number POST2014/8171)

New Classification and Generative Model for Medical Visual Question Answering

Medical images are playing an important role in the medical domain. A mature medical visual question answering system can aid diagnosis, but there is no satisfactory method to solve this comprehensive problem so far. Considering that there are many different types of questions, we propose a model called CGMVQA, including classification and answer generation capabilities to turn this complex problem into multiple simple problems in this paper. We adopt data augmentation on images and tokenization on texts. We use pre-trained ResNet152 to extract image features and add three kinds of embeddings together to deal with texts. We reduce the parameters of the multi-head self-attention transformer to cut the computational cost down. We adjust the masking and output layers to change the functions of the model. This model establishes new state-of-the-art results: 0.640 of classification accuracy, 0.659 of word matching and 0.678 of semantic similarity in ImageCLEF 2019 VQA-Med data set. It suggests that the CGMVQA is effective in medical visual question answering and can better assist doctors in clinical analysis and diagnosis

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

Few-Shot Visual Grounding for Natural Human-Robot Interaction

Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments. In such dynamic environments, the robot needs to understand the intention of the user to accomplish a task successfully. Towards addressing this point, we propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. Unlike most grounding methods that tackle the challenge using pre-trained object detectors via a two-stepped process, we develop a single stage zero-shot model that is able to provide predictions in unseen data. We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets. Experimental results showed that the proposed model performs well in terms of accuracy and speed, while showcasing robustness to variation in the natural language input.Comment: 6 pages, 4 figures, ICARSC2021 accepte