Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario

Bekel H, Heidemann G, Ritter H (2005)
Neural Networks 18(2005 Special Iss.): 566-574.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA
DOI
URN
urn:nbn:de:0070-pub-27142865
Autor*in
Bekel, Holger; Heidemann, Gunther; Ritter, HelgeUniBi
Einrichtung
Technische Fakultät > AG Neuroinformatik
Abstract / Bemerkung
We present an approach for the convenient labeling of image patches gathered from an unrestricted environment. The system is employed for a mobile Augmented Reality (AR) gear: While the user walks around with the head-mounted AR-gear, context-free modules for focus-of-attention permanently sample the most “interesting” image patches. After this acquisition phase, a Self-Organizing Map (SOM) is trained on the complete set of patches, using combinations of MPEG-7 features as a data representation. The SOM allows visualization of the sampled patches and an easy manual sorting into categories. With very little effort, the user can compose a training set for a classifier, thus, unknown objects can be made known to the system. We evaluate the system for COIL-imagery and demonstrate that a user can reach satisfying categorization within few steps, even for image data sampled from walking in an office environment.
Erscheinungsjahr
2005
Zeitschriftentitel
Neural Networks
Band
18
Ausgabe
2005 Special Iss.
Seite(n)
566-574
ISSN
0893-6080
Page URI
https://pub.uni-bielefeld.de/record/2714286

Zitieren

Bekel H, Heidemann G, Ritter H. Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario. Neural Networks. 2005;18(2005 Special Iss.):566-574.
Bekel, H., Heidemann, G., & Ritter, H. (2005). Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario. Neural Networks, 18(2005 Special Iss.), 566-574. doi:10.1016/j.neunet.2005.06.040
Bekel, Holger, Heidemann, Gunther, and Ritter, Helge. 2005. “Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario”. Neural Networks 18 (2005 Special Iss.): 566-574.
Bekel, H., Heidemann, G., and Ritter, H. (2005). Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario. Neural Networks 18, 566-574.
Bekel, H., Heidemann, G., & Ritter, H., 2005. Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario. Neural Networks, 18(2005 Special Iss.), p 566-574.
H. Bekel, G. Heidemann, and H. Ritter, “Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario”, Neural Networks, vol. 18, 2005, pp. 566-574.
Bekel, H., Heidemann, G., Ritter, H.: Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario. Neural Networks. 18, 566-574 (2005).
Bekel, Holger, Heidemann, Gunther, and Ritter, Helge. “Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario”. Neural Networks 18.2005 Special Iss. (2005): 566-574.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
Dieses Objekt ist durch das Urheberrecht und/oder verwandte Schutzrechte geschützt. [...]
Volltext(e)
Name
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-06T09:18:29Z
MD5 Prüfsumme
1e54cdc1b94ccdc2a9c0596397afb261


3 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

A three-dimensional analysis of the morphological evolution and locomotor behaviour of the carnivoran hind limb.
Martín-Serra A, Figueirido B, Palmqvist P., BMC Evol Biol 14(), 2014
PMID: 24927753
A preliminary investigation assessing the viability of classifying hand postures in seniors.
Tavakolan M, Xiao ZG, Menon C., Biomed Eng Online 10(), 2011
PMID: 21906316
A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics.
Oyana TJ., EURASIP J Bioinform Syst Biol (), 2010
PMID: 20689710

25 References

Daten bereitgestellt von Europe PubMed Central.


AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Focus-of-attention from local color symmetries.
Heidemann G., IEEE Trans Pattern Anal Mach Intell 26(7), 2004
PMID: 18579942

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
The internal representation of solid shape with respect to vision.
Koenderink JJ, van Doorn AJ., Biol Cybern 32(4), 1979
PMID: 454703
Self-organized formation of topologically correct feature maps
Kohonen, Biological Cybernetics 43(), 1982

Kohonen, 1995
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors.
Laaksonen J, Koskela M, Oja E., IEEE Trans Neural Netw 13(4), 2002
PMID: 18244480
Color and texture descriptors
Manjunath, IEEE Transactions on Circuits and Systems for Video Technology 11(6), 2001
Topology representing networks
Martinetz, Neural Networks 7(3), 1994
SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition.
Mel BW., Neural Comput 9(4), 1997
PMID: 9161022

AUTHOR UNKNOWN, 0
Visual learning and recognition of 3-D objects from appearance
Murase, International Journal of Computer Vision 14(), 1995

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
The mpeg-7 visual standard for content description-an overview.
Sikora, IEEE Transactions on Circuits and Systems for Video Technology 11(6), 2001
Self-organizing maps and learning vector quantization for feature sequences
Somervuo, Neural Processing Letters 10(2), 1999
Application of self-organizing maps (som) to auger electron spectroscopy (aes)
Tokutaka, Surface and Interface Analysis 27(8), 1999
Neural correlates of attention in primate visual cortex.
Treue S., Trends Neurosci. 24(5), 2001
PMID: 11311383
Som-based data visualization methods
Vesanto, Intelligent Data Analysis 3(2), 1999

Villmann, 2003
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 16112547
PubMed | Europe PMC

Suchen in

Google Scholar