Sparse representation for human gait recognition

Sparse representation for human gait recognition

Sparsity-based algorithms recently have received great interests from statistics, signal processing, machine learning as well as computer vision. In this master thesis, it discusses the sparse representation based algorithms for computer vision problem, including the independent sparse representatio...

Full description

Saved in:
Bibliographic Details
Main Author: Zeng, Zinan
Other Authors: Xu Dong
Format: Theses and Dissertations
Language:English
Published: 2011
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Online Access:http://hdl.handle.net/10356/46243
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Sparsity-based algorithms recently have received great interests from statistics, signal processing, machine learning as well as computer vision. In this master thesis, it discusses the sparse representation based algorithms for computer vision problem, including the independent sparse representation (ISR), locality-constraint coding, group sparse representation (GSR). Based on these existing algorithms, two new algorithms referred to as locality-constrain group sparse representation (LGSR) and multiple-kernel group sparse representation (MKGSR) are proposed. Comprehensive experiments for Human Gait Recognition (HGR) using USF HumanID Gait database show that the two newly proposed methods, LGSR and MKGSR respectively achieve the best Rank-1 and Rank-5 recognition accuracy.

Similar Items