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Evaluation of Feature Selection and Model Training Strategies for Object Category Recognition

Ali, Haider and Marton, Zoltan-Csaba (2014) Evaluation of Feature Selection and Model Training Strategies for Object Category Recognition. In: IEEE International Conference on Intelligent Robots and Systems, 5036- 5042. International Conference on Intelligent Robots and Systems (IROS), Sept. 14–18, 2014, Chicago, Illinois. DOI: 10.1109/IROS.2014.6943278 ISSN 1552-3098

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Official URL: http://www.iros2014.org/


Several methods for object category recognition in RGB-D images have been reported in literature. These methods are typically tested under the same conditions (which we can consider a ``domain'' in a restricted sense) such as viewing angles, distances to the object as well as lightening conditions on which they are trained. However, in practical applications one often has to deal with previously unseen domains. In this paper, we investigate the effect of domain change on the performance of object category recognition methods. We use the public RGB-D Object Dataset from Lai~\textit{et~al.} \cite{RGBD_ICRA2011} for training, and for testing we introduce the DLR-RGB-D dataset, representing a similar, but different domain. The data present in both datasets holds various object instances grouped into general object categories. Object category detectors are trained using the objects of one domain and tested on the objects of the other domain. We then explored how do different 3D features perform when the model trained on the source domain is applied on the target domain, and evaluated two feature selection strategies. In our experiments we show that a domain change can have significant impact on the model's accuracy, and present results for improving the results by increasing the variability of the objects in the training domain. Finally, we discuss the relevance of the descriptors and the properties they capture.

Item URL in elib:https://elib.dlr.de/93644/
Document Type:Conference or Workshop Item (Speech)
Title:Evaluation of Feature Selection and Model Training Strategies for Object Category Recognition
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ali, HaiderHaider.Ali (at) dlr.deUNSPECIFIED
Marton, Zoltan-CsabaZoltan.Marton (at) dlr.dehttps://orcid.org/0000-0002-3035-493X
Date:September 2014
Journal or Publication Title:IEEE International Conference on Intelligent Robots and Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/IROS.2014.6943278
Page Range:5036- 5042
Keywords:object categorization, cross-domain learning, feature selection, domain adaptation, RGBD object databases
Event Title:International Conference on Intelligent Robots and Systems (IROS)
Event Location:Chicago, Illinois
Event Type:international Conference
Event Dates:Sept. 14–18, 2014
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Deposited By: Marton, Dr. Zoltan-Csaba
Deposited On:08 Jan 2015 11:41
Last Modified:31 Jul 2019 19:50

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