Combining Two Visual Cognition Systems Using Confidence Radius and Combinatorial Fusion

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.

[1]  D. Frank Hsu,et al.  Joint decision making in visual cognition using Combinatorial Fusion Analysis , 2011, IEEE 10th International Conference on Cognitive Informatics and Cognitive Computing (ICCI-CC'11).

[2]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[3]  Ursula Casanova,et al.  Are two heads are better than one? , 1988 .

[4]  D. Frank Hsu,et al.  Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval , 2005, Information Retrieval.

[5]  D. Frank Hsu,et al.  Combining multiple visual cognition systems for joint decision-making using combinatorial fusion , 2012, 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing.

[6]  Chuan Yi Tang,et al.  Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction , 2007, IEEE Transactions on NanoBioscience.

[7]  James M. Hillis,et al.  Combining Sensory Information: Mandatory Fusion Within, but Not Between, Senses , 2002, Science.

[8]  S. Gepshtein,et al.  The combination of vision and touch depends on spatial proximity. , 2005, Journal of vision.

[9]  Damian M. Lyons,et al.  Combining multiple scoring systems for target tracking using rank-score characteristics , 2009, Inf. Fusion.

[10]  D. Frank Hsu,et al.  Rank-Score Characteristics (RSC) Function and Cognitive Diversity , 2010, Brain Informatics.

[11]  Hatim A. Zariwala,et al.  Neural correlates, computation and behavioural impact of decision confidence , 2008, Nature.

[12]  D. Frank Hsu,et al.  Fusion of two visual perception systems utilizing cognitive diversity , 2013, 2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing.

[13]  R. Blake,et al.  Neural bases of binocular rivalry , 2006, Trends in Cognitive Sciences.

[14]  M. Ernst.  Learning to integrate arbitrary signals from vision and touch. , 2007, Journal of vision.

[15]  P. Latham,et al.  References and Notes Supporting Online Material Materials and Methods Figs. S1 to S11 References Movie S1 Optimally Interacting Minds R�ports , 2022 .

[16]  Paul B. Kantor,et al.  Predicting the effectiveness of Naïve data fusion on the basis of system characteristics , 2000 .

[17]  Asher Koriat,et al.  When Are Two Heads Better than One and Why? , 2012, Science.

[18]  D. Frank Hsu,et al.  Consensus Scoring Criteria for Improving Enrichment in Virtual Screening , 2005, J. Chem. Inf. Model..

[19]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[20]  M. Morrone,et al.  Touch disambiguates rivalrous perception at early stages of visual analysis , 2010, Current Biology.

[21]  Marc O. Ernst.  Decisions Made Better , 2010, Science.

[22]  D. Frank Hsu,et al.  Comparative Study of Joint Decision-Making on Two Visual Cognition Systems Using Combinatorial Fusion , 2012, AMT.