---
_id: '1401'
abstract:
- lang: eng
text: 'The human ability to recognize objects in complex scenes has driven research
in the computer vision field over couple of decades. This thesis focuses on the
object recognition task in images. That is, given the image, we want the computer
system to be able to predict the class of the object that appears in the image.
A recent successful attempt to bridge semantic understanding of the image perceived
by humans and by computers uses attribute-based models. Attributes are semantic
properties of the objects shared across different categories, which humans and
computers can decide on. To explore the attribute-based models we take a statistical
machine learning approach, and address two key learning challenges in view of
object recognition task: learning augmented attributes as mid-level discriminative
feature representation, and learning with attributes as privileged information.
Our main contributions are parametric and non-parametric models and algorithms
to solve these frameworks. In the parametric approach, we explore an autoencoder
model combined with the large margin nearest neighbor principle for mid-level
feature learning, and linear support vector machines for learning with privileged
information. In the non-parametric approach, we propose a supervised Indian Buffet
Process for automatic augmentation of semantic attributes, and explore the Gaussian
Processes classification framework for learning with privileged information. A
thorough experimental analysis shows the effectiveness of the proposed models
in both parametric and non-parametric views.'
acknowledgement: "I would like to thank my supervisor, Christoph Lampert, for guidance
throughout my studies and for patience in transforming me into a scientist, and
my thesis committee, Chris Wojtan and Horst Bischof, for their help and advice.
\r\n\r\nI would like to thank Elisabeth Hacker who perfectly assisted all my administrative
needs and was always nice and friendly to me, and the campus team for making the
IST Austria campus my second home. \r\nI was honored to collaborate with brilliant
researchers and to learn from their experience. Undoubtedly, I learned most of all
from Novi Quadrianto: brainstorming our projects and getting exciting results was
the most enjoyable part of my work – thank you! I am also grateful to David Knowles,
Zoubin Ghahramani, Daniel Hernández-Lobato, Kristian Kersting and Anastasia Pentina
for the fantastic projects we worked on together, and to Kristen Grauman and Adriana
Kovashka for the exceptional experience working with user studies. I would like
to thank my colleagues at IST Austria and my office mates who shared their happy
moods, scientific breakthroughs and thought-provoking conversations with me: Chao,
Filip, Rustem, Asya, Sameh, Alex, Vlad, Mayu, Neel, Csaba, Thomas, Vladimir, Cristina,
Alex Z., Avro, Amelie and Emilie, Andreas H. and Andreas E., Chris, Lena, Michael,
Ali and Ipek, Vera, Igor, Katia. Special thanks to Morten for the countless games
of table soccer we played together and the tournaments we teamed up for: we will
definitely win next time:) A very warm hug to Asya for always being so inspiring
and supportive to me, and for helping me to increase the proportion of female computer
scientists in our group. "
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
citation:
ama: 'Sharmanska V. Learning with attributes for object recognition: Parametric
and non-parametrics views. 2015. doi:10.15479/at:ista:1401'
apa: 'Sharmanska, V. (2015). Learning with attributes for object recognition:
Parametric and non-parametrics views. Institute of Science and Technology
Austria. https://doi.org/10.15479/at:ista:1401'
chicago: 'Sharmanska, Viktoriia. “Learning with Attributes for Object Recognition:
Parametric and Non-Parametrics Views.” Institute of Science and Technology Austria,
2015. https://doi.org/10.15479/at:ista:1401.'
ieee: 'V. Sharmanska, “Learning with attributes for object recognition: Parametric
and non-parametrics views,” Institute of Science and Technology Austria, 2015.'
ista: 'Sharmanska V. 2015. Learning with attributes for object recognition: Parametric
and non-parametrics views. Institute of Science and Technology Austria.'
mla: 'Sharmanska, Viktoriia. Learning with Attributes for Object Recognition:
Parametric and Non-Parametrics Views. Institute of Science and Technology
Austria, 2015, doi:10.15479/at:ista:1401.'
short: 'V. Sharmanska, Learning with Attributes for Object Recognition: Parametric
and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.'
date_created: 2018-12-11T11:51:48Z
date_published: 2015-04-01T00:00:00Z
date_updated: 2023-09-07T11:40:11Z
day: '01'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
- _id: GradSch
doi: 10.15479/at:ista:1401
file:
- access_level: open_access
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creator: dernst
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language:
- iso: eng
main_file_link:
- url: http://users.sussex.ac.uk/~nq28/viktoriia/Thesis_Sharmanska.pdf
month: '04'
oa: 1
oa_version: Published Version
page: '144'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '5806'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: 'Learning with attributes for object recognition: Parametric and non-parametrics
views'
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2015'
...