Rot-SiLA: A Novel Ensemble Classification Approach Based on Rotation Forest and Similarity Learning Using Nearest Neighbor Algorithm

Shaheryar, Muhammad; Khalid, Maryam; Qamar, Ali Mustafa

doi:10.1109/icmla.2013.16

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In addition, there is no literature review present to select the value of . Some of the previous works, have been using fixed number a of group of classifiers while some for variable size of [40]. Rodrguez et al [34] have used the fixed value of =10.…”

Section: The Sensitivity Of Parametersmentioning

confidence: 99%

Human Action Recognition via Depth Maps Body Parts of Action

2018

KSII TIIS

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Human actions can be recognized from depth sequences. In the proposed algorithm, we initially construct depth, motion maps (DMM) by projecting each depth frame onto three orthogonal Cartesian planes and add the motion energy for each view. The body part of the action (BPoA) is calculated by using bounding box with an optimal window size based on maximum spatial and temporal changes for each DMM. Furthermore, feature vector is constructed by using BPoA for each human action view. In this paper, we employed an ensemble based learning approach called Rotation Forest to recognize different actions Experimental results show that proposed method has significantly outperforms the state-of-the-art methods on Microsoft Research (MSR) Action 3D and MSR DailyActivity3D dataset.

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Section: The Sensitivity Of Parametersmentioning

confidence: 99%

Human Action Recognition via Depth Maps Body Parts of Action

2018

KSII TIIS

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“…Muhammad Shaheryar et. al [6] proposed an novel ensemble classifier Rot-SiLA which is developed by combining Rotation Forest algorithm and SiLA. Random Forest includes decision trees based on feature extraction where Principal Component Analysis (PCA) is used to rotate the feature subsets.…”

Section: Literature Surveymentioning

confidence: 99%