Automatic image annotation is the process of assigning keywords to digital images depending
on the content information. In one sense, it is a mapping from the visual content information
to the semantic context information. In this thesis, we propose a novel approach for
automatic image annotation problem, where the annotation is formulated as a multivariate
mapping from a set of independent descriptor spaces, representing a whole image, to a set
of words, representing class labels. For this purpose, a hierarchical annotation architecture,
named as HANOLISTIC (Hierarchical Image Annotation System Using Holistic Approach),
is dened with two layers. At the rst layer, called level-0 annotator, each annotator is fed
by a set of distinct descriptor, extracted from the whole image. This enables us to represent
the image at each annotator by a dierent visual property of a descriptor. Since, we use
the whole image, the problematic segmentation process is avoided. Training of each annotator
is accomplished by a supervised learning paradigm, where each word is represented
by a class label. Note that, this approach is slightly dierent then the classical training
approaches, where each data has a unique label. In the proposed system, since each image
has one or more annotating words, we assume that an image belongs to more than one
class. The output of the level-0 annotators indicate the membership values of the words
in the vocabulary, to belong an image. These membership values from each annotator is,
then, aggregated at the second layer by using various rules, to obtain meta-layer annotator. The rules, employed in this study, involves summation and/or weighted summation of the
output of layer-0 annotators. Finally, a set of words from the vocabulary is selected based
on the ranking of the output of meta-layer. The hierarchical annotation system proposed in
this thesis outperforms state of the art annotation systems based on segmental and holistic
approaches. The proposed system is examined in-depth and compared to the other systems
in the literature by means of using several performance criteria.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12609230/index.pdf |
| Date | 01 January 2008 |
| Creators | Oztimur, Ozge |
| Contributors | Yarman-vural, Fatos Tunay |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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