Open AccessAction recognition based on motion of oriented magnitude patterns and feature selection
Author(s) -
Phan HaiHong,
Vu NgocSon,
Nguyen VuLam,
Quoy Mathias
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0282
Subject(s) - discriminative model , pattern recognition (psychology) , artificial intelligence , redundancy (engineering) , feature selection , principal component analysis , feature (linguistics) , mutual information , computer science , dimensionality reduction , support vector machine , dimension (graph theory) , mathematics , philosophy , operating system , pure mathematics , linguistics
Here, the authors introduce a novel system which incorporates the discriminative motion of oriented magnitude patterns (MOMP) descriptor into simple yet efficient techniques. The authors’ descriptor both investigates the relations of the local gradient distributions in neighbours among consecutive image sequences and characterises information changing across different orientations. The proposed system has two main contributions: (i) the authors adopt feature post‐processing principal component analysis followed by vector of locally aggregated descriptors encoding to de‐correlate MOMP descriptor and reduce the dimension in order to speed up the algorithm; (ii) then the authors include the feature selection (i.e. statistical dependency, mutual information, and minimal redundancy maximal relevance) to find out the best feature subset to improve the performance and decrease the computational expense in classification through support vector machine techniques. Experiment results on four data sets, Weizmann (98.4%), KTH (96.3%), UCF Sport (82.0%), and HMDB51 (31.5%), prove the efficiency of the authors’ algorithm.