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)

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. 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Overview of the ImageCLEF 2013 robot vision task

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