Digital multimedia content production and the amount of content present all
over the world have exploded in the recent years. The consequences of this fact
can be observed everywhere in many different forms, to exemplify, huge digital
video archives of broadcasting companies, commercial image archives, virtual
museums, etc. In order for these sources to be useful and accessible, this
technological advance must be accompanied by the effective techniques of
indexing and retrieval. The most effective way of indexing is the one providing a
basis for retrieval in terms of semantic concepts, upon which ordinary users of
multimedia databases base their queries. On the other hand, semantic
classification of images using low-level features is a challenging problem.
Combining experts with different classifier structures, trained by MPEG-7low-level color and texture descriptors, is examined as a solution alternative. For
combining different classifiers and features, advanced decision mechanisms are
proposed, which utilize basic expert combination strategies in different settings.
Each of these decision mechanisms, namely Single Feature Combination (SFC),
Multiple Feature Direct Combination (MFDC), and Multiple Feature Cascaded
Combination (MFCC) enjoy significant classification performance improvements
over single experts. Simulations are conducted on eight different visual semantic
classes, resulting in accuracy improvements between 3.5-6.5%, when they are
compared with the best performance of single expert systems.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/4/1124353/index.pdf |
| Date | 01 January 2003 |
| Creators | Soysal, Medeni |
| Contributors | Alatan, Aydin A. |
| 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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