Action Recognition Based on Local Fisher Discriminant Analysis and Mix Encoding

Action recognition has been one of the most popular fields of computer vision. This paper presents a novel approach to action recognition problem using the dimension reduction method, local fisher discriminant analysis, to reduce the dimension of feature descriptors as the preprocessing step after f...

Full description

Saved in:
Bibliographic Details
Published in2016 International Conference on Virtual Reality and Visualization (ICVRV) pp. 16 - 23
Main Authors Lijun Li, Shuling Dai
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Action recognition
Computer vision
Dimension reduction
Encoding
Feature encoding
Feature extraction
Histograms
Kernel
Trajectory
Visualization
Online AccessGet full text

Cover

More Information
Summary:Action recognition has been one of the most popular fields of computer vision. This paper presents a novel approach to action recognition problem using the dimension reduction method, local fisher discriminant analysis, to reduce the dimension of feature descriptors as the preprocessing step after feature extraction. We propose to use sparse matrix and randomized kd-tree to modify and accelerate the standard local fisher discriminant analysis and propose the modified local fisher discriminant analysis. We also propose an effective feature encoding called mix encoding to combine fisher vector encoding and locality-constrained linear coding to obtain video representations. The experiments show the methods clearly improve the recognition accuracy. Experimental results show our method outperforms our baseline method and can be the state of the art in the KTH dataset.
DOI:10.1109/ICVRV.2016.12