Abstract
When combining decisions made by two separate visual cognition systems, simple average and weighted average using statistical means are used. In this paper, we extend the visual cognition system to become a scoring system using Combinatorial Fusion Analysis (CFA) based on each of the statistical means M1, M2, and M3 respectively. Eight experiments are conducted, structured CFA framework. Our main results are: (a) If the two individual systems are relatively good, the combined systems perform better, and (b) rank combination is often better than score combination. A unique way of making better joint decisions in visual cognition using Combinatorial Fusion is demonstrated.
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Batallones, A., Sanchez, K., Mott, B., McMunn-Coffran, C., Hsu, D.F. (2013). Combining Two Visual Cognition Systems Using Confidence Radius and Combinatorial Fusion. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_8
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DOI: https://doi.org/10.1007/978-3-319-02753-1_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02752-4
Online ISBN: 978-3-319-02753-1
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