Authors: Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and Tomaso Poggio, Member, IEEE Reporter: Lê Ngọc Minh In Computational vision course, Master of Cognitive science, Trento University

1. Robust Object Recognition with
Cortex-Like Mechanisms
Authors: Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian
Riesenhuber, and Tomaso Poggio, Member, IEEE
Reporter: Lê Ngọc Minh

2. Content
1. How is it like?
2. Where does it come from?
3. What is it?
1. Performance
2. Authors' contribution
3. Unsolved problems
4. Where will it go?
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3. How is it like?
● As demonstrated by Neocognitron
– Youtube: http://www.youtube.com/watch?
v=Qil4kmvm2Sw
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4. Where does it come
from?
● 1959, 1962: Simple and Complex cells
(Torsten Wiesel and David Hubel)
● 1980: Neocognitron (Fukushima)
● 1999: HMAX
● 2005: Feedforward model of the ventral
stream in primate visual cortex (Serre et. al)
● 2007: Cortex-like algorithm (Serre et. al)
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5. What is it?
● New factors in the
algorithm compare to
Neocognitron:
– Gabor filter for simple cells
(J. G. Daugman, 1985)
– Max for complex cells
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6. Performance
● Comparable or superior to
other systems in accuracy
● Better than SIFT features in
more general categorization
tasks
● Universal feature: general,
independent of training
Results Obtained with 1,000 C2 Features Combined
examples, avoid over-fitting with SVM or GentleBoost (boost) Classifiers and
problem Comparison with Existing Systems (Benchmark)
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7. Authors' contributions
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8. Authors' contributions
● Full-fledged, working implementation of a
cognitive model into computer
● Broad and multifaceted evaluation of the
algorithm that demonstrates its strength
● The discovery of universal feature set
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9. Unsolved problems
● There's still room for further development:
parameter tuning, more complex
architecture,...
● The main limitation of this approach is speed:
typically, tens of seconds, depending on the
size of the input image
● Many following researches try to alleviate this
problem.
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10. New developments
● Exploit special hardware to
achieve more efficient
computation
– Jim Mutch, 2010: GPUs
– Al Maashri, 2011: FPGA
● Better model of simple cells:
– George Azzopardi, 2012: CORF
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11. References
● D. H. Hubel and T. N. Wiesel Receptive Fields of Single Neurones in the
Cat's Striate Cortex J. Physiol. pp. 574-591 (148) 1959
● D. H. Hubel and T. N. Wiesel "Receptive Fields, Binocular Interaction
and Functional Architecture in the Cat's Visual Cortex" J. Physiol. 160
pp. 106-154 1962
● K. Fukushima: "Neocognitron: A self-organizing neural network model
for a mechanism of pattern recognition unaffected by shift in position",
Biological Cybernetics, 36[4], pp. 193-202 (April 1980).
● HMAX: M. Riesenhuber and T. Poggio, Hierarchical Models of Object
Recognition in Cortex, 1999
● A Theory of Object Recognition: Computations and Circuits in the
Feedforward Path of the Ventral Stream in Primate Visual Cortex (T.
Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, T. Poggio, 2005)
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12. References
● Robust Object Recognition with Cortex-Like Mechanisms (Thomas
Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and
Tomaso Poggio, 2007)
● A hardware architecture for accelerating neuromorphic vision
algorithms, Al Maashri, A, 2011
● CNS: a GPU-based framework for simulating cortically-organized
networks Jim Mutch, Ulf Knoblich, and Tomaso Poggio, 2010
● George Azzopardi, Nicolai Petkov, A CORF computational model of a
simple cell that relies on LGN input outperforms the Gabor function
model, 2012
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13. THANK YOU!
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