---
res:
  bibo_abstract:
  - '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.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Viktoriia
      foaf_name: Sharmanska, Viktoriia
      foaf_surname: Sharmanska
      foaf_workInfoHomepage: http://www.librecat.org/personId=2EA6D09E-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-0192-9308
  bibo_doi: 10.15479/at:ista:1401
  dct_date: 2015^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2663-337X
  dct_language: eng
  dct_publisher: Institute of Science and Technology Austria@
  dct_title: 'Learning with attributes for object recognition: Parametric and non-parametrics
    views@'
...