University of Illinois Urbana-Champaign

Ontology-based image categorization

Xu, Ning

Content Files
Ning_Xu.pdf

Permalink

https://hdl.handle.net/2142/73014

Description

Title
Ontology-based image categorization
Author(s)
Xu, Ning
Issue Date
2015-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2015-01-21T19:56:03Z
Keyword(s)
  • Ontology
  • weak attributes
  • semantics
  • image categorization
  • semantic splitting
  • multiple-instance learning
Abstract
In this thesis, we study how semantics can improve image categorization. Previous image categorization approaches mostly neglect semantics, which has two major limitations. First, object classes have semantic overlaps. For example, “sedan” is a specific kind of “car”. However, previous approaches treat “sedan” and “car” as independent and train a classifier to distinguish them, which is unreasonable. Second, image features used for classification are unified for different object classes. But this is at odds with the human perception system, which is believed to use different features for distinct objects. For example, the features used for differentiating “sedan” from “bike” should be distinct from the features used for differentiating “sedan” from “hatchback”. In this thesis, we leverage semantic ontologies to solve the aforementioned problems. We propose a Random Forest based algorithm in which the splitting of tree nodes is first determined by semantic relations among categories. Then weak attributes are automatically learned by multiple-instance learning to capture visual similarities in a hierarchical way; i.e., different local features are learned to classify objects at different semantic levels. Overall, our approach imitates the human visual system and is more advanced than previous non-ontology based approaches. We test our approach on two fine-grained image categorization datasets. The experimental results demonstrate that our approach not only outperforms the state-of-the-art approaches but also identifies semantically meaningful visual features.
Graduation Semester
2014-12
Permalink
http://hdl.handle.net/2142/73014
Copyright and License Information
Copyright 2014 Ning Xu

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering