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Author Scheirer, Walter J.
Title Extreme value theory-based methods for visual recognition [electronic resource] / Walter J. Scheirer.
Publication Info [San Rafael, California] : Morgan & Claypool, 2017.



Descript 1 PDF (xv, 115 pages) : illustrations.
Note Part of: Synthesis digital library of engineering and computer science.
Contents 1. Extrema and visual recognition -- 1.1 An alternative to central tendency modeling -- 1.2 Background -- 1.3 Extreme value theory for recognition -- 1.4 Decision making in machine learning -- 1.5 Organization --
2. A brief introduction to statistical extreme value theory -- 2.1 Basic concepts -- 2.2 The extreme value theorem -- 2.3 Distributions in the EVT family -- 2.3.1 Gumbel distribution -- 2.3.2 Fréchet distribution -- 2.3.3 Weibull distribution -- 2.3.4 Generalized extreme value distribution -- 2.3.5 Rayleigh distribution -- 2.3.6 Generalized Pareto distribution -- 2.4 Tail size estimation -- 2.5 The i.i.d. assumption and visual data --
3. Post-recognition score analysis -- 3.1 Failure prediction for recognition systems -- 3.2 Meta-recognition -- 3.2.1 A formal model of recognition -- 3.2.2 Meta-recognition as hypothesis testing -- 3.2.3 Weibull-based meta-recognition -- 3.2.4 Validation tools for meta-recognition -- 3.3 Uses of meta-recognition for visual recognition --
4. Recognition score normalization -- 4.1 Goals of good recognition score normalization -- 4.2 W-score normalization -- 4.3 Empirical evaluation of W-score fusion -- 4.4 Other instantiations of EVT normalization -- 4.4.1 GEV-based normalization: extreme value sample consensus -- 4.4.2 GEV-based normalization: GEV-Kmeans -- 4.4.3 Pareto-based normalization: image retrieval as outlier detection -- 4.4.4 Pareto-based normalization: visual inspection via anomaly detection --
5. Calibration of supervised machine learning algorithms -- 5.1 Goals of calibration for decision making -- 5.2 Probability of exclusion: multi-attribute spaces -- 5.3 Open set recognition -- 5.3.1 Probability of inclusion: PI-SVM -- 5.3.2 Probability of inclusion and exclusion: W-SVM -- 5.3.3 Sparse representation-based open set recognition -- 5.3.4 EVT calibration for deep networks: OpenMax --
6. Summary and future directions -- Bibliography -- Author's biography.
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
.
ISBN 9781627057035 ebook
9781627057004 print
Standard # 10.2200/S00756ED1V01Y201701COV010 doi
Click on the terms below to find similar items in the catalogue
Author Scheirer, Walter J.
Series Synthesis lectures on computer vision, # 10
Synthesis digital library of engineering and computer science.
Synthesis lectures on computer vision ; # 10. 2153-1064
Subject Computer vision.
Extreme value theory.
Visual perception.
Descript 1 PDF (xv, 115 pages) : illustrations.
Note Part of: Synthesis digital library of engineering and computer science.
Contents 1. Extrema and visual recognition -- 1.1 An alternative to central tendency modeling -- 1.2 Background -- 1.3 Extreme value theory for recognition -- 1.4 Decision making in machine learning -- 1.5 Organization --
2. A brief introduction to statistical extreme value theory -- 2.1 Basic concepts -- 2.2 The extreme value theorem -- 2.3 Distributions in the EVT family -- 2.3.1 Gumbel distribution -- 2.3.2 Fréchet distribution -- 2.3.3 Weibull distribution -- 2.3.4 Generalized extreme value distribution -- 2.3.5 Rayleigh distribution -- 2.3.6 Generalized Pareto distribution -- 2.4 Tail size estimation -- 2.5 The i.i.d. assumption and visual data --
3. Post-recognition score analysis -- 3.1 Failure prediction for recognition systems -- 3.2 Meta-recognition -- 3.2.1 A formal model of recognition -- 3.2.2 Meta-recognition as hypothesis testing -- 3.2.3 Weibull-based meta-recognition -- 3.2.4 Validation tools for meta-recognition -- 3.3 Uses of meta-recognition for visual recognition --
4. Recognition score normalization -- 4.1 Goals of good recognition score normalization -- 4.2 W-score normalization -- 4.3 Empirical evaluation of W-score fusion -- 4.4 Other instantiations of EVT normalization -- 4.4.1 GEV-based normalization: extreme value sample consensus -- 4.4.2 GEV-based normalization: GEV-Kmeans -- 4.4.3 Pareto-based normalization: image retrieval as outlier detection -- 4.4.4 Pareto-based normalization: visual inspection via anomaly detection --
5. Calibration of supervised machine learning algorithms -- 5.1 Goals of calibration for decision making -- 5.2 Probability of exclusion: multi-attribute spaces -- 5.3 Open set recognition -- 5.3.1 Probability of inclusion: PI-SVM -- 5.3.2 Probability of inclusion and exclusion: W-SVM -- 5.3.3 Sparse representation-based open set recognition -- 5.3.4 EVT calibration for deep networks: OpenMax --
6. Summary and future directions -- Bibliography -- Author's biography.
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
.
ISBN 9781627057035 ebook
9781627057004 print
Standard # 10.2200/S00756ED1V01Y201701COV010 doi
Author Scheirer, Walter J.
Series Synthesis lectures on computer vision, # 10
Synthesis digital library of engineering and computer science.
Synthesis lectures on computer vision ; # 10. 2153-1064
Subject Computer vision.
Extreme value theory.
Visual perception.

Subject Computer vision.
Extreme value theory.
Visual perception.
Descript 1 PDF (xv, 115 pages) : illustrations.
Note Part of: Synthesis digital library of engineering and computer science.
Contents 1. Extrema and visual recognition -- 1.1 An alternative to central tendency modeling -- 1.2 Background -- 1.3 Extreme value theory for recognition -- 1.4 Decision making in machine learning -- 1.5 Organization --
2. A brief introduction to statistical extreme value theory -- 2.1 Basic concepts -- 2.2 The extreme value theorem -- 2.3 Distributions in the EVT family -- 2.3.1 Gumbel distribution -- 2.3.2 Fréchet distribution -- 2.3.3 Weibull distribution -- 2.3.4 Generalized extreme value distribution -- 2.3.5 Rayleigh distribution -- 2.3.6 Generalized Pareto distribution -- 2.4 Tail size estimation -- 2.5 The i.i.d. assumption and visual data --
3. Post-recognition score analysis -- 3.1 Failure prediction for recognition systems -- 3.2 Meta-recognition -- 3.2.1 A formal model of recognition -- 3.2.2 Meta-recognition as hypothesis testing -- 3.2.3 Weibull-based meta-recognition -- 3.2.4 Validation tools for meta-recognition -- 3.3 Uses of meta-recognition for visual recognition --
4. Recognition score normalization -- 4.1 Goals of good recognition score normalization -- 4.2 W-score normalization -- 4.3 Empirical evaluation of W-score fusion -- 4.4 Other instantiations of EVT normalization -- 4.4.1 GEV-based normalization: extreme value sample consensus -- 4.4.2 GEV-based normalization: GEV-Kmeans -- 4.4.3 Pareto-based normalization: image retrieval as outlier detection -- 4.4.4 Pareto-based normalization: visual inspection via anomaly detection --
5. Calibration of supervised machine learning algorithms -- 5.1 Goals of calibration for decision making -- 5.2 Probability of exclusion: multi-attribute spaces -- 5.3 Open set recognition -- 5.3.1 Probability of inclusion: PI-SVM -- 5.3.2 Probability of inclusion and exclusion: W-SVM -- 5.3.3 Sparse representation-based open set recognition -- 5.3.4 EVT calibration for deep networks: OpenMax --
6. Summary and future directions -- Bibliography -- Author's biography.
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
.
ISBN 9781627057035 ebook
9781627057004 print
Standard # 10.2200/S00756ED1V01Y201701COV010 doi

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