The feature parallelism model of visual recognition
In this work, the Feature Parallelism Model of visual recognition, which addresses the parallel nature of the human brain compared to the hierarchal (serial) brain model, was studied. First, its accuracy rate and training time were compared to those of DeepFace, a leading industry algorithm for f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science & Engineering Research Support Society (SERSC)
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/56655/1/The%20Feature%20Parallelism%20Model%20of%20Visual%20Recognition.pdf http://irep.iium.edu.my/56655/ http://www.sersc.org/journals/IJMUE/vol12_no2_2017/13.pdf |
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| Summary: | In this work, the Feature Parallelism Model of visual recognition, which addresses the
parallel nature of the human brain compared to the hierarchal (serial) brain model, was
studied. First, its accuracy rate and training time were compared to those of DeepFace, a
leading industry algorithm for face recognition. Both models were trained using
ImageNet object recognition dataset. Accuracy rates were almost the same, around 57%
top-1 error rate and 33% top-5 error rate. Training time for feature parallelism model
has dropped to 21% less than that of Deep Face. Second, we have investigated feature
parallelism model under depth, i.e., when adding more layers along the horizontal axis.
We have tested the model with 5, 6, 7, and 8 layers respectively; we found that the best
results both in terms of accuracy rates and training time were obtained with the sixlayered
model. Although the training time enhancement was only a few milliseconds when
going from 5 to 6 layers, it has worsened significantly when going from 6 to 7 layers. In
fact the training time has tripled, i.e., training time of the 7- layers model is three times of
that of the 6- layers model. It continues to worsen by a fewer rate with the 8- layers
model. Similarly, accuracy rate was better with the 6- layers model by about 1% of that of
the 5- layers model; however, it has worsened by more than 5% whenever we add more
layers above six. We consider those results are biologically plausible, as they conform to
the biological fact that the cerebral cortex is organized in 6- layers. We’ve concluded that
the organization of parallel processing units into 6- layers, either in our brains or in
artificial vision systems, may enhance both processing time and accuracy rates. |
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