EXTREME VALUE THEORY-BASED METHODS FOR VISUAL RECOGNITION by Walter J. Scheirer, Gerard Medioni, Sven Dickinson ( 9781627057004 )

Item Details

Title:

EXTREME VALUE THEORY-BASED METHODS FOR VISUAL RECOGNITION

By:

Walter J. Scheirer, Gerard Medioni, Sven Dickinson

Format:

Paperback

List price:	£55.95
We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10:

1627057005

ISBN 13:

9781627057004

Publisher:

MORGAN & CLAYPOOL PUBLISHERS

Pub. date:

15 February, 2017

Series:

Synthesis Lectures on Computer Vision

Pages:

131

Description:

Begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. 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.

Synopsis:

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.

Publication:

Imprint:

Morgan and Claypool Life Sciences

Returns:

Non-returnable

Some other items by this author:

ACTIVITY UNDERSTANDING (PB)

ADVANCED METHODS FOR FACE-BASED BIOMETRICS (PB)

ARTICULATED MODEL FITTING (PB)

BELIEF PROPAGATION (PB)

BOOSTING-BASED FACE DETECTION AND ADAPTATION (PB)

COMPONENT ANALYSIS (PB)