Titlebook: Human Action Analysis with Randomized Trees; Gang Yu,Junsong Yuan,Zicheng Liu Book 2015 The Author(s) 2015 Action Categorization.Branch-an

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書目名稱Human Action Analysis with Randomized Trees讀者反饋




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2191-8112 rious applications.Practical examples and applications will 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, su

meditation

plasma-cells

Human Action Prediction with Multiclass Balanced Random Forest,ilities. The prediction is done simultaneously for multiple classes, which saves both the memory and computational cost. The experiments show that our algorithm significantly outperforms the state-of-the-art for the human activity prediction problem.

Handedness

Ptosis

GLUE

Supervised Trees for Human Action Recognition and Detection,human actions. In this chapter, we characterize a video as a collection of spatio-temporal interest points, and locate actions via searching for spatio-temporal video subvolumes of the highest mutual information score toward each action class. A random forest is constructed to efficiently generate d

悄悄移動(dòng)

Unsupervised Trees for Human Action Search,a very fast action retrieval system which can effectively locate the subvolumes similar to the query video. Random-indexing-trees-based visual vocabularies are introduced for the database indexing. By increasing the number of vocabularies, the large intra-class variance problem can be relieved despi

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Propagative Hough Voting to Leverage Contextual Information, insufficient training data are provided. To address this limitation, we propose propagative Hough voting in this chapter. Instead of training a discriminative classifier for local feature voting, we first match labeled feature points to unlabeled feature points, then propagate the label and sptatio

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Human Action Prediction with Multiclass Balanced Random Forest,ing a spatial-temporal implicit shape model (STISM), which characterizes the space-time structure of the sparse local features extracted from a video. The recognition of human activities is accomplished by pattern matching through STISM. To enable efficient and robust matching, we propose a new rand