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Human action analysis with randomize...

Liu, Zicheng.

Human action analysis with randomized trees[electronic resource] /

レコード種別:	言語・文字資料 (印刷物) : 単行資料
[NT 15000414] null:	511.52
タイトル / 著者:	Human action analysis with randomized trees/ by Gang Yu, Junsong Yuan, Zicheng Liu.
著者:	Yu, Gang.
その他の著者:	Yuan, Junsong.
出版された:	Singapore : : Springer Singapore :, 2015.
記述:	viii, 83 p. : : ill., digital ;; 24 cm.
含まれています:	Springer eBooks
主題:	Trees (Graph theory)
主題:	Human behavior - Research.
主題:	Engineering.
主題:	Signal, Image and Speech Processing.
主題:	Image Processing and Computer Vision.
主題:	Probability Theory and Stochastic Processes.
国際標準図書番号 (ISBN) :	9789812871671 (electronic bk.)
国際標準図書番号 (ISBN) :	9789812871664 (paper)
[NT 15000228] null:	Introduction to Human Action Analysis -- Supervised Trees for Human Action Recognition and Detection -- Unsupervised Trees for Human Action Search -- Propagative Hough Voting to Leverage Contextual Information -- Human Action Prediction with Multi-class Balanced Random Forest -- Conclusion.
[NT 15000229] null:	This book will provide a comprehensive overview on human action analysis with randomized trees. It will cover both the supervised random trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast method for space-time interest point matching. When labeled data is minimal as in the case of example-based action search, unsupervised random trees is used to leverage the unlabelled data. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction. We will also describe techniques for space-time action localization including branch-and-bound sub-volume search and propagative Hough voting.
電子資源:	http://dx.doi.org/10.1007/978-981-287-167-1

Human action analysis with randomized trees[electronic resource] /
Yu, Gang.

Human action analysis with randomized trees[electronic resource] /by Gang Yu, Junsong Yuan, Zicheng Liu. - Singapore :Springer Singapore :2015. - viii, 83 p. :ill., digital ;24 cm. - Springer briefs in electrical and computer engineering. Signal processing,2191-8112. - Springer briefs in electrical and computer engineering.Signal processing..

Introduction to Human Action Analysis -- Supervised Trees for Human Action Recognition and Detection -- Unsupervised Trees for Human Action Search -- Propagative Hough Voting to Leverage Contextual Information -- Human Action Prediction with Multi-class Balanced Random Forest -- Conclusion.

This book will provide a comprehensive overview on human action analysis with randomized trees. It will cover both the supervised random trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast method for space-time interest point matching. When labeled data is minimal as in the case of example-based action search, unsupervised random trees is used to leverage the unlabelled data. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction. We will also describe techniques for space-time action localization including branch-and-bound sub-volume search and propagative Hough voting.

ISBN: 9789812871671 (electronic bk.)

Standard No.: 10.1007/978-981-287-167-1doiSubjects--Topical Terms:

602417
Trees (Graph theory)

LC Class. No.: QA166.2

Dewey Class. No.: 511.52

Human action analysis with randomized trees[electronic resource] /
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245 1 0 $a Human action analysis with randomized trees $h [electronic resource] / $c by Gang Yu, Junsong Yuan, Zicheng Liu.
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490 1 $a Springer briefs in electrical and computer engineering. Signal processing, $x 2191-8112
505 0 $a Introduction to Human Action Analysis -- Supervised Trees for Human Action Recognition and Detection -- Unsupervised Trees for Human Action Search -- Propagative Hough Voting to Leverage Contextual Information -- Human Action Prediction with Multi-class Balanced Random Forest -- Conclusion.
520 $a This book will provide a comprehensive overview on human action analysis with randomized trees. It will cover both the supervised random trees and the unsupervised random trees. When there are sufficient amount of labeled data available, supervised random trees provides a fast method for space-time interest point matching. When labeled data is minimal as in the case of example-based action search, unsupervised random trees is used to leverage the unlabelled data. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction. We will also describe techniques for space-time action localization including branch-and-bound sub-volume search and propagative Hough voting.
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650 2 4 $a Image Processing and Computer Vision. $3 463967
650 2 4 $a Probability Theory and Stochastic Processes. $3 463894
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700 1 $a Liu, Zicheng. $3 602415
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830 0 $a Springer briefs in electrical and computer engineering. $p Signal processing. $3 602416
856 4 0 $u http://dx.doi.org/10.1007/978-981-287-167-1
950 $a Engineering (Springer-11647)

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