EAN: 9783031006890

SYNTHESIS LECTURES ON COMPUTER VISION EXTREME...

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.

Aktuelle Preise zur EAN 9783031006890

Produktinformationen zur 9783031006890