RT Journal Article
T1 Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images
A1 Santos Saavedra, David
A1 López López, Eric
A1 Pardo López, Xosé Manuel
A1 Iglesias Rodríguez, Roberto
A1 Barro Ameneiro, Senén
A1 Fernández Vidal, Xosé Ramón
K1 Scene recognition
K1 Image collection summarization
K1 Meaningful images
AB Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from which is likely feasible that the correct identification of the scene can be made. Our proposal works with a heuristic metric based on the detection of key points in 3D meshes (Harris 3D). This metric is later used to build a model that combines a Minimum Spanning Tree and a Support Vector Machine (SVM). We have performed an extensive number of experiments through which we have addressed (a) the search for efficient visual descriptors, (b) the analysis of the extent to which our heuristic metric resembles the human criteria for relevance and, finally, (c) the experimental validation of our complete proposal. In the experiments, we have used both a public image database and images collected at our research center
PB MDPI
YR 2019
FD 2019
LK http://hdl.handle.net/10347/21247
UL http://hdl.handle.net/10347/21247
LA eng
NO Santos, D.; Lopez-Lopez, E.; Pardo, X.M.; Iglesias, R.; Barro, S.; Fdez-Vidal, X.R. Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images. Sensors 2019, 19, 4024
NO This research has received financial support from AEI/FEDER (European Union) [TIN2017-90135-R], Xunta de Galicia [ED431F 2018/02], as well as the Conselleria de Cultura, Educacion e Ordenacion Universitaria accreditation 2016–2019) [ED431G/08], and reference competitive groups: [ED431C 2018/29] and [ED431C 2017/69], and the European Regional Development Fund (ERDF)
DS Minerva
RD 23 abr 2026