Indoor Scene Recognition based on Weighted Voting Schemes
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00334777" target="_blank" >RIV/68407700:21230/19:00334777 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/68407700:21730/19:00334777
Výsledek na webu
<a href="https://doi.org/10.1109/ECMR.2019.8870931" target="_blank" >https://doi.org/10.1109/ECMR.2019.8870931</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ECMR.2019.8870931" target="_blank" >10.1109/ECMR.2019.8870931</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Indoor Scene Recognition based on Weighted Voting Schemes
Popis výsledku v původním jazyce
Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. “office”, “classroom” or “kitchen”) can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.
Název v anglickém jazyce
Indoor Scene Recognition based on Weighted Voting Schemes
Popis výsledku anglicky
Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. “office”, “classroom” or “kitchen”) can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20204 - Robotics and automatic control
Návaznosti výsledku
Projekt
<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2019
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of European Conference on Mobile Robots
ISBN
978-1-7281-3605-9
ISSN
—
e-ISSN
—
Počet stran výsledku
6
Strana od-do
—
Název nakladatele
Czech Technical University
Místo vydání
Prague
Místo konání akce
Prague
Datum konání akce
4. 8. 2019
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000558081900027