On the combination of two visual cognition systems using combinatorial fusion

When combining decisions made by two separate visual cognition systems, statistical means such as simple average (M1) and weighted average (M2 and M3), incorporating the confidence level of each of these systems have been used. Although combination using these means can improve each of the individual systems, it is not known when and why this can happen. By extending a visual cognition system to become a scoring system based on each of the statistical means M1, M2, and M3 respectively, the problem of combining visual cognition systems is transformed to the problem of combining multiple scoring systems. In this paper, we examine the combined results in terms of performance and diversity using combinatorial fusion, and study the issue of when and why a combined system can be better than individual systems. A data set from an experiment with twelve trials is analyzed. The findings demonstrated that combination of two visual cognition systems, based on weighted means M2 or M3, can improve each of the individual systems only when both of them have relatively good performance and they are diverse.

[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]  Nan-Suey Liu,et al.  Mach number distribution and plume direction prediction of a rocket thruster operating at four different combustion chamber pressures , 2004, Journal of Vision.

[4]  D. Frank Hsu,et al.  Sensor Feature Selection and Combination for Stress Identification Using Combinatorial Fusion , 2013 .

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

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

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

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

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

[10]  Xing Zhang,et al.  A skeleton pruning algorithm based on information fusion , 2013, Pattern Recognit. Lett..

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

[12]  Soon Myoung Chung,et al.  Combination of Multiple Feature Selection Methods for Text Categorization by using Combinatorial Fusion Analysis and Rank-Score Characteristic , 2013, Int. J. Artif. Intell. Tools.

[13]  Clive R. Bagshaw.  Are two heads better than one? , 1987, Nature.

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

[15]  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.

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

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

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

[19]  Christina Schweikert,et al.  Combining multiple ChIP-seq peak detection systems using combinatorial fusion , 2012, BMC Genomics.

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

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

[22]  D. Frank Hsu,et al.  Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems , 2006 .

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

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

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

[26]  Chuan Yi Tang,et al.  Comparing System Selection Methods for the Combinatorial Fusion of Multiple Retrieval Systems , 2013, J. Interconnect. Networks.

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

[28]  Hui-Huang Hsu,et al.  Advanced Data Mining Technologies in Bioinformatics , 2006 .

[29]  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.

[30]  Shiro Usui,et al.  Brain and Health Informatics , 2013, Lecture Notes in Computer Science.

[31]  Paul B. Kantor,et al.  Predicting the effectiveness of naïve data fusion on the basis of system characteristics , 2000, J. Am. Soc. Inf. Sci..

[32]  D. Frank Hsu,et al.  Combining Two Visual Cognition Systems Using Confidence Radius and Combinatorial Fusion , 2013, Brain and Health Informatics.

[33]  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 .

[34]  YONG DENG,et al.  Combining Multiple Sensor Features for stress Detection using Combinatorial Fusion , 2012, J. Interconnect. Networks.