Extreme Value Theory-Based Methods for Visual Recognition

Walter J. Scheirer (Autor)

Buch | Softcover

XV, 115 Seiten

2017
Springer International Publishing (Verlag)
978-3-031-00689-0 (ISBN)

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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.

Walter J. Scheirer is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Previously, he was a postdoctoral fellow at Harvard University, with affiliations in the School of Engineering and Applied Sciences, Department of Molecular and Cellular Biology and Center for Brain Science, and the director of research & development at Securics, Inc., an early-stage company producing innovative computer vision-based solutions. He received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University. Dr. Scheirer has extensive experience in the areas of computer vision and human biometrics, with an emphasis on advanced learning techniques. His overarching research interest is the fundamental problem of recognition, including the representations and algorithms supporting solutions to it.

Preface.- Acknowledgments.- Figure Credits.- Extrema and Visual Recognition.- A Brief Introduction to Statistical Extreme Value Theory.- Post-recognition Score Analysis.- Recognition Score Normalization.- Calibration of Supervised Machine Learning Algorithms.- Summary and Future Directions.- Bibliography.- Author's Biography.

Erscheinungsdatum	06.06.2022
Reihe/Serie	Synthesis Lectures on Computer Vision
Zusatzinfo	XV, 115 p.
Verlagsort	Cham
Sprache	englisch
Maße	191 x 235 mm
Gewicht	264 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
ISBN-10	3-031-00689-5 / 3031006895
ISBN-13	978-3-031-00689-0 / 9783031006890
Zustand	Neuware