---
OA_place: publisher
_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:<a href="https://doi.org/10.15479/at:ista:1401">10.15479/at:ista:1401</a>'
  apa: 'Sharmanska, V. (2015). <i>Learning with attributes for object recognition:
    Parametric and non-parametrics views</i>. Institute of Science and Technology
    Austria. <a href="https://doi.org/10.15479/at:ista:1401">https://doi.org/10.15479/at:ista:1401</a>'
  chicago: 'Sharmanska, Viktoriia. “Learning with Attributes for Object Recognition:
    Parametric and Non-Parametrics Views.” Institute of Science and Technology Austria,
    2015. <a href="https://doi.org/10.15479/at:ista:1401">https://doi.org/10.15479/at:ista:1401</a>.'
  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. <i>Learning with Attributes for Object Recognition:
    Parametric and Non-Parametrics Views</i>. Institute of Science and Technology
    Austria, 2015, doi:<a href="https://doi.org/10.15479/at:ista:1401">10.15479/at:ista:1401</a>.'
  short: 'V. Sharmanska, Learning with Attributes for Object Recognition: Parametric
    and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.'
corr_author: '1'
date_created: 2018-12-11T11:51:48Z
date_published: 2015-04-01T00:00:00Z
date_updated: 2026-04-09T14:25:49Z
day: '01'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
- _id: GradSch
doi: 10.15479/at:ista:1401
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fulldoi: https://doi.org/10.15479/at:ista:1401
has_accepted_license: '1'
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: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2015'
...