Semantic classification of images using low-level features is a challenging problem. Combining experts with different classifier structures, trained by MPEG-7 low-level color and texture descriptors is examined as a solution alternative. For combining different classifiers and features, two advanced decision mechanisms are proposed, one of which enjoys a significant classification performance improvement. Simulations are conducted on 8 different visual semantic classes, resulting in accuracy improvements between 3.5-6.5%, when they are compared with the best performance of single classifier systems. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Soysal, M., & Alatan, A. A. (2003). Combining MPEG-7 based visual experts for reaching semantics. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2849, 66–75. https://doi.org/10.1007/978-3-540-39798-4_11
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