loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Heydar Maboudi Afkham ; Stefan Carlsson and Josephine Sullivan

Affiliation: KTH, Sweden

Keyword(s): Feature inference, Latent models, Clustering.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Clustering ; Computer Vision, Visualization and Computer Graphics ; Graphical and Graph-Based Models ; Image Understanding ; Pattern Recognition ; Regression ; Theory and Methods

Abstract: 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 leve l 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.222.104.206

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Maboudi Afkham, H.; Carlsson, S. and Sullivan, J. (2012). IMPROVING FEATURE LEVEL LIKELIHOODS USING CLOUD FEATURES. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-8425-99-7; ISSN 2184-4313, SciTePress, pages 431-437. DOI: 10.5220/0003777904310437

@conference{icpram12,
author={Heydar {Maboudi Afkham}. and Stefan Carlsson. and Josephine Sullivan.},
title={IMPROVING FEATURE LEVEL LIKELIHOODS USING CLOUD FEATURES},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2012},
pages={431-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003777904310437},
isbn={978-989-8425-99-7},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - IMPROVING FEATURE LEVEL LIKELIHOODS USING CLOUD FEATURES
SN - 978-989-8425-99-7
IS - 2184-4313
AU - Maboudi Afkham, H.
AU - Carlsson, S.
AU - Sullivan, J.
PY - 2012
SP - 431
EP - 437
DO - 10.5220/0003777904310437
PB - SciTePress