理化学研究所Bio-mimetic Control Research Center, RIKEN
Guided learning from images using an uncertain granular model and
bio-mimicry of the human fovea
Jonathan Rossiter
Toshiharu Mukai
Institute of Physical and Chemical Research
RIKEN, Japan
+
University of Bristol
理化学研究所2 Bio-mimetic Control
Research Center, RIKEN
Bio-mimetic AI
Studying and copying human intelligence and behaviours in artificially intelligent systems
• Perceptions and sensing• Representations• Reasoning• Learning • Adapting and updating
理化学研究所3 Bio-mimetic Control
Research Center, RIKEN
Motivation: human-like robotics
Rescue Robot• Hazardous• Real-time/on-site training• Remote control • Autonomous Intelligence
Guided operation• Dumb
Guided learning• Dumb, but at least it’s learning…
理化学研究所4 Bio-mimetic Control
Research Center, RIKEN
Consider only image domain Learning from image data (goal is high level model)
Crisp image data• Conventional features
• Crisp values
But what is uncertain image data?• High level concepts encroaching on low level data
• Degrees of applicability/relevance across larger scale features
So need to combine both crisp image data and uncertain image data
crisp image data → induction → uncertainty model
uncertain image data → induction → uncertainty model
理化学研究所5 Bio-mimetic Control
Research Center, RIKEN
Learning with granules
Size(P) = { small : 0.3, medium : 0.7}
Cost(P) = { reasonable : 0.2, cheap : 0.8}
GP = { small^reasonable: 0.3*0.2, small^cheap: 0.3*0.8, medium^reasonable: 0.7*0.2, medium^ cheap: 0.7*0.8 }
理化学研究所6 Bio-mimetic Control
Research Center, RIKEN
Learning with granules A granule is thus a discrete fuzzy set G over the universe
of cross-product labels L:G = {l L : m [0, 1]}
where:
L =×(Ki | i = 1, . . . , n) and Ki is a single fuzzy set label (e.g. small, medium, etc) In this paper the aggregation operation used to turn
training instances Gj into the model GM is simply :
GM = Norm(j Gj)
And with applicability values this becomes:
GM = Norm(j Gj × aj)
理化学研究所7 Bio-mimetic Control
Research Center, RIKEN
Human visual system – from light to electricity
理化学研究所8 Bio-mimetic Control
Research Center, RIKEN
Light sensors in the retina
理化学研究所9 Bio-mimetic Control
Research Center, RIKEN
Vision and active learning
理化学研究所10 Bio-mimetic Control
Research Center, RIKEN
理化学研究所11 Bio-mimetic Control
Research Center, RIKEN
Fovea- based region focus
Applicability
Fovea scaling
Relative Absolute
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Research Center, RIKEN
理化学研究所13 Bio-mimetic Control
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Unit applicability
34.6%Relative scale Gaussian-type
applicability ( = 0.3)
49.5%
Absolute scale Gaussian-type applicability ( = 0.4 over 5%)
48.9%
理化学研究所14 Bio-mimetic Control
Research Center, RIKEN
Trapezoidal applicabilityx = 0
83.9%
Relative scale Gaussian-typeapplicability ( = 0.2)
83.9%
Unit applicability
82.1%
理化学研究所15 Bio-mimetic Control
Research Center, RIKEN
Conclusions Fovea-like applicability functions better
• Natural• Incorporates into linguistic inductive learning
Not clear whether relative or absolute functions are better
• But, with relative applicability we need not worry about absolute scale. Easier.
Further research • Optimize the choice of applicability function • Incorporating such a system into tools to aid medical diagnosis
and into vision systems for rescue robots operating in hazardous environments.
理化学研究所16 Bio-mimetic Control
Research Center, RIKEN
Thank you
理化学研究所17 Bio-mimetic Control
Research Center, RIKEN
Image feature scale
理化学研究所18 Bio-mimetic Control
Research Center, RIKEN
High level + low level information
Low level• Sensor based• Data rich• Crisp/precise
High level • Taxononomical• Conceptual• Linguistic• Uncertain
Fusion in training• High + low best of both worlds
理化学研究所19 Bio-mimetic Control
Research Center, RIKEN
Updating robot vision
Human guidance of robot • Varied environments
理化学研究所20 Bio-mimetic Control
Research Center, RIKEN
Human-like perception
Goldberg and terminus of perception• Image features in abstract to terminus• Kind of high-level from low level
Also have high level information• Modifies/constrains our views of image data
• Examples
Humans combine both high and low level image information
• Good place to look for inspiration • Bio-mimetic high level approaches to reasoning with
information and sensor data