3 research outputs found

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

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    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 ConsejerĂ­a de EducaciĂłn, Cultura y Deportes of the JCCM regional government through project PPII-2014-015-P (grant number DPI2013-40534-R). Jesus MartĂ­nez-GĂłmez is also funded by the JCCM (grant number POST2014/8171)

    ImageCLEF 2014: Overview and analysis of the results

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    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

    Overview of the ImageCLEF 2013 robot vision task

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    This article describes the RobotVision@ImageCLEF 2013 challenge, which addresses two problems: place classification and object recognition. Participants of the challenge were asked to classify rooms on the basis of image sequences captured by cameras mounted on a mobile robot. They were also asked to detect the appearance or lack of several objects. The proposals of the participants had to answer two questions: \where are you?" (I am in the elevator, in the toilet, etc.) and \which objects can you see?" (I can see a table and a chair but not a computer) when presented with a test sequence. The number of times a specific set of object appears in a frame was not considered but if they appeared or not in it. The test sequence was acquired within the same environment but with different lighting conditions than the training sequences. The main novelty of the 2013 edition of the task is the object recognition problem. For both problems: place classification and object recognition, depth and visual images were provided. Moreover, participants were allowed to take advantage from the temporal continuity of the test sequence. The winner of the 2013 edition of the Robot Vision task was the MIAR ICT group, from China
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