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Books > Computing & IT > Applications of computing > Image processing
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Extreme Value Theory-Based Methods for Visual Recognition (Paperback)
Loot Price: R1,428
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Extreme Value Theory-Based Methods for Visual Recognition (Paperback)
Series: Synthesis Lectures on Computer Vision
Expected to ship within 10 - 15 working days
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A common feature of many approaches to modeling sensory statistics
is an emphasis on capturing the "average." From early
representations in the brain, to highly abstracted class categories
in machine learning for classification tasks, central-tendency
models based on the Gaussian distribution are a seemingly natural
and obvious choice for modeling sensory data. However, insights
from neuroscience, psychology, and computer vision suggest an
alternate strategy: preferentially focusing representational
resources on the extremes of the distribution of sensory inputs.
The notion of treating extrema near a decision boundary as features
is not necessarily new, but a comprehensive statistical theory of
recognition based on extrema is only now just emerging in the
computer vision literature. This book begins by introducing the
statistical Extreme Value Theory (EVT) for visual recognition. In
contrast to central-tendency modeling, it is hypothesized that
distributions near decision boundaries form a more powerful model
for recognition tasks by focusing coding resources on data that are
arguably the most diagnostic features. EVT has several important
properties: strong statistical grounding, better modeling accuracy
near decision boundaries than Gaussian modeling, the ability to
model asymmetric decision boundaries, and accurate prediction of
the probability of an event beyond our experience. The second part
of the book uses the theory to describe a new class of machine
learning algorithms for decision making that are a measurable
advance beyond the state-of-the-art. This includes methods for
post-recognition score analysis, information fusion,
multi-attribute spaces, and calibration of supervised machine
learning algorithms.
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