Extreme Value Theory-Based Methods for Visual Recognition (Record no. 84913)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03974nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01817-6 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163723.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2017 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031018176 |
| -- | 978-3-031-01817-6 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006 |
| 100 1# - AUTHOR NAME | |
| Author | Scheirer, Walter J. |
| 245 10 - TITLE STATEMENT | |
| Title | Extreme Value Theory-Based Methods for Visual Recognition |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 115 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Computer Vision, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Digital techniques. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01817-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2017. |
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| -- | text |
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| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Image processing |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Pattern recognition systems. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Vision. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automated Pattern Recognition. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2153-1064 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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