Automatic image annotation is the process of automatically producing words to de-
scribe the content for a given image. It provides us with a natural means of semantic
indexing for content based image retrieval. In this thesis, two novel automatic image
annotation systems targeting di& / #64256 / erent types of annotated data are proposed. The
& / #64257 / rst system, called Supervised Ensemble of Visual Descriptors (SEVD), is trained
on a set of annotated images with prede& / #64257 / ned class labels. Then, the system auto-
matically annotates an unknown sample depending on the classi& / #64257 / cation results. The
second system, called Unsupervised Ensemble of Visual Descriptors (UEVD), assumes
no class labels. Therefore, the annotation of an unknown sample is accomplished by
unsupervised learning based on the visual similarity of images. The available auto-
matic annotation systems in the literature mostly use a single set of features to train
a single learning architecture. On the other hand, the proposed annotation systems
utilize a novel model of image representation in which an image is represented with
a variety of feature sets, spanning an almost complete visual information comprising
color, shape, and texture characteristics. In both systems, a separate learning entity is
trained for each feature set and these entities are gathered under an ensemble learning
approach. Empirical results show that both SEVD and UEVD outperform some of
the state-of-the-art automatic image annotation systems in equivalent experimental
setups.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/12607443/index.pdf |
| Date | 01 August 2006 |
| Creators | Akbas, Emre |
| 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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