| 000 | 05454nam a22005295i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01809-1 | ||
| 003 | DE-He213 | ||
| 005 | 20240730164227.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2010 sz | s |||| 0|eng d | ||
| 020 |
_a9783031018091 _9978-3-031-01809-1 |
||
| 024 | 7 |
_a10.1007/978-3-031-01809-1 _2doi |
|
| 050 | 4 | _aTA1501-1820 | |
| 050 | 4 | _aTA1634 | |
| 072 | 7 |
_aUYT _2bicssc |
|
| 072 | 7 |
_aCOM016000 _2bisacsh |
|
| 072 | 7 |
_aUYT _2thema |
|
| 082 | 0 | 4 |
_a006 _223 |
| 100 | 1 |
_aZhang, Cha. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983130 |
|
| 245 | 1 | 0 |
_aBoosting-Based Face Detection and Adaptation _h[electronic resource] / _cby Cha Zhang, Zhengyou Zhang. |
| 250 | _a1st ed. 2010. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010. |
|
| 300 |
_aXII, 132 p. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aSynthesis Lectures on Computer Vision, _x2153-1064 |
|
| 505 | 0 | _aA Brief Survey of the Face Detection Literature -- Cascade-based Real-Time Face Detection -- Multiple Instance Learning for Face Detection -- Detector Adaptation -- Other Applications -- Conclusions and Future Work. | |
| 520 | _aFace detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. We then present two multiple instance learning schemesfor face detection, multiple instance learning boosting (MILBoost) and winner-take-all multiple category boosting (WTA-McBoost). MILBoost addresses the uncertainty in accurately pinpointing the location of the object being detected, while WTA-McBoost addresses the uncertainty in determining the most appropriate subcategory label for multiview object detection. Both schemes can resolve the ambiguity of the labeling process and reduce outliers during training, which leads to improved detector performances. In many applications, a detector trained with generic data sets may not perform optimally in a new environment. We propose detection adaption, which is a promising solution for this problem. We present an adaptation scheme based on the Taylor expansion of the boosting learning objective function, and we propose to store the second order statistics of the generic training data for future adaptation. We show that with a small amount of labeled data in the new environment, the detector'sperformance can be greatly improved. We also present two interesting applications where boosting learning was applied successfully. The first application is face verification for filtering and ranking image/video search results on celebrities. We present boosted multi-task learning (MTL), yet another boosting learning algorithm that extends MILBoost with a graphical model. Since the available number of training images for each celebrity may be limited, learning individual classifiers for each person may cause overfitting. MTL jointly learns classifiers for multiple people by sharing a few boosting classifiers in order to avoid overfitting. The second application addresses the need of speaker detection in conference rooms. The goal is to find who is speaking, given a microphone array and a panoramic video of the room. We show that by combining audio and visual features in a boosting framework, we can determine the speaker's position very accurately. Finally, we offer our thoughts on future directions for face detection. Table of Contents: A Brief Survey of the Face Detection Literature / Cascade-based Real-Time Face Detection / Multiple Instance Learning for Face Detection / Detector Adaptation / Other Applications / Conclusions and Future Work. | ||
| 650 | 0 |
_aImage processing _xDigital techniques. _94145 |
|
| 650 | 0 |
_aComputer vision. _983133 |
|
| 650 | 0 |
_aPattern recognition systems. _93953 |
|
| 650 | 1 | 4 |
_aComputer Imaging, Vision, Pattern Recognition and Graphics. _931569 |
| 650 | 2 | 4 |
_aComputer Vision. _983136 |
| 650 | 2 | 4 |
_aAutomated Pattern Recognition. _931568 |
| 700 | 1 |
_aZhang, Zhengyou. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983137 |
|
| 710 | 2 |
_aSpringerLink (Online service) _983140 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031006814 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031029370 |
| 830 | 0 |
_aSynthesis Lectures on Computer Vision, _x2153-1064 _983141 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01809-1 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85458 _d85458 |
||