CRIS Current Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishWe propose a method to perform automatic segmentation of 3D scenes based on a standard classifier, whose learning model is continuously improved by means of new samples, and a grouping stage, that enforces local consistency among classified labels. The new samples are automatically delivered to the system by a feedback loop based on a feature selection approach that exploits the outcome of the grouping stage. By experimental results on several datasets we demonstrate that the proposed online learning paradigm is effective in increasing the accuracy of the whole 3D segmentation thanks to the improvement of the learning model of the classifier by means of newly acquired, unsupervised data.
| Titolo: | Online Learning for automatic segmentation of 3D data | |
| Autore/i: | TOMBARI, FEDERICO; DI STEFANO, LUIGI; S. Giardino | |
| Autore/i Unibo: | ||
| Anno: | 2011 | |
| Serie: | ||
| Titolo del libro: | Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on | |
| Pagina iniziale: | 4857 | |
| Pagina finale: | 4864 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/IROS.2011.6094649 | |
| Abstract: | We propose a method to perform automatic segmentation of 3D scenes based on a standard classifier, whose learning model is continuously improved by means of new samples, and a grouping stage, that enforces local consistency among classified labels. The new samples are automatically delivered to the system by a feedback loop based on a feature selection approach that exploits the outcome of the grouping stage. By experimental results on several datasets we demonstrate that the proposed online learning paradigm is effective in increasing the accuracy of the whole 3D segmentation thanks to the improvement of the learning model of the classifier by means of newly acquired, unsupervised data. | |
| Data prodotto definitivo in UGOV: | 17-giu-2013 | |
| Data stato definitivo: | 25-nov-2015 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11585/106129
Copyright © 2021