Comparative study of joint decision-making on two visual cognition systems using combinatorial fusion

Authors:
Amy Batallones;Cameron McMunn-Coffran;Kilby Sanchez;Brian Mott;D. Frank Hsu
Affiliations:
Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY;Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY;Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY;Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY;Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY
Venue:
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Year:
2012

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Abstract

In processing multimedia technologies or decision-making in visual cognition systems, combination by both simple average and weighted average are used. In this paper, we extend each visual cognition system to a scoring system using Combinatorial Fusion Analysis (CFA). We investigate the performance of the combined system in terms of individual system's performance and confidence. Twelve experiments are conducted and our main results are: (a) The combined systems perform better only if the two individual systems are relatively good, and (b) overall, rank combination is better than score combination. In addition, we compare the three types of averages: simple average M1, weighted average M2 using σ, and weighted average M3 using σ2, where σ is related to confidence of each system. Our results exhibit a novel way to better make joint decisions in visual cognition using Combinatorial Fusion.