Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Improving feature level likelihoods using cloud features
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2012 (English)In: ICPRAM - Proc. Int. Conf. Pattern Recogn. Appl. Methods, 2012, 431-437 p.Conference paper, Published paper (Refereed)
Abstract [en]

The performance of many computer vision methods depends on the quality of the local features extracted from the images. For most methods the local features are extracted independently of the task and they remain constant through the whole process. To make features more dynamic and give models a choice in the features they can use, this work introduces a set of intermediate features referred as cloud features. These features take advantage of part-based models at the feature level by combining each extracted local feature with its close by local feature creating a cloud of different representations for each local features. These representations capture the local variations around the local feature. At classification time, the best possible representation is pulled out of the cloud and used in the calculations. This selection is done based on several latent variables encoded within the cloud features. The goal of this paper is to test how the cloud features can improve the feature level likelihoods. The focus of the experiments of this paper is on feature level inference and showing how replacing single features with equivalent cloud features improves the likelihoods obtained from them. The experiments of this paper are conducted on several classes of MSRCv1 dataset.

Place, publisher, year, edition, pages
2012. 431-437 p.
Series
ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 2
Keyword [en]
Clustering, Feature inference, Latent models, Classification time, Data sets, Feature level, Latent variable, Local feature, Local variations, Whole process, Experiments, Computer vision
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-128697Scopus ID: 2-s2.0-84862197327ISBN: 9789898425980 (print)OAI: oai:DiVA.org:kth-128697DiVA: diva2:653136
Conference
1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012, 6 February 2012 through 8 February 2012, Vilamoura, Algarve
Note

QC 20131003

Available from: 2013-10-03 Created: 2013-09-16 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

No full text

Scopus

Search in DiVA

By author/editor
Maboudi Afkham, HeydarCarlsson, StefanSullivan, Josephine
By organisation
Computer Vision and Active Perception, CVAP
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 71 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf