Artificial Visual Cortex and Random Search for Object Categorization
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- @Article{Olague:2019:ACC,
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author = "G. Olague and E. Clemente and D. E. Hernandez and
A. Barrera and M. Chan-Ley and S. Bakshi",
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journal = "IEEE Access",
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title = "Artificial Visual Cortex and Random Search for Object
Categorization",
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year = "2019",
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volume = "7",
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pages = "54054--54072",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ACCESS.2019.2912792",
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ISSN = "2169-3536",
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abstract = "Brain modeling is a research area within computer
science devoted to the study of complex and dynamic
computing algorithms that imitate brain function
regarding the information processing properties of the
structures that make up the nervous system. The
computational and mathematical structures are composed
of interacting modules, whose coordination aims to
enhance their problem-solving capabilities. The
computational models of the visual cortex use
non-trivial interactions between a large number of
components. In this paper, we propose a hierarchical
structure that mimics the information flow and
transformations that take place in the human brain.
This paper describes a virtual system composed of an
artificial dorsal pathway-or {"}where{"} stream-and an
artificial ventral pathway-or {"}what{"} stream-both
are fused to recreate an artificial visual cortex. In
previous work, the model was refined through genetic
programming to enhance its performance over challenging
object recognition tasks. The system finds good
solutions during the initial stage of the genetic and
evolutionary search. In this paper, the goal is to show
that a random search can discover numerous
heterogeneous functions that are applied to a
hierarchical structure of our virtual brain. Thus, the
proposal presents two key ideas: (1) the concept of
function composition in combination with a hierarchical
structure leads to outstanding object recognition
programs, and; (2) multiple random runs of the search
process can discover optimal functions. The
experimental results provide evidence that high
recognition rates could be achieved in well-known
object categorization problems; consequently, this
paper corroborates the importance of the hierarchical
computational structure described in the neuroscience
literature.",
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notes = "Also known as \cite{8694984}",
- }
Genetic Programming entries for
Gustavo Olague
E Clemente
D E Hernandez
Aaron Barrera
Mariana Chan-Ley
S Bakshi
Citations