Combining MPEG-7 based visual experts for reaching semantics

Date

2003-01-01

Author

Soysal, M
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Support vector machine, Product rule , Semantic classis , Median rule , Homogeneous texture

URI

https://hdl.handle.net/11511/56074

Collections

Department of Electrical and Electronics Engineering, Article