| Popis: |
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. |
