Abstract
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.
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Soysal, M., Alatan, A.A. (2003). Combining MPEG-7 Based Visual Experts for Reaching Semantics. In: García, N., Salgado, L., Martínez, J.M. (eds) Visual Content Processing and Representation. VLBV 2003. Lecture Notes in Computer Science, vol 2849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39798-4_11
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DOI: https://doi.org/10.1007/978-3-540-39798-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20081-9
Online ISBN: 978-3-540-39798-4
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