Open AccessVideo based object representation and classification using multiple covariance matrices
Author(s) -
Yurong Zhang,
Quan Liu
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0176598
Subject(s) - pattern recognition (psychology) , discriminative model , artificial intelligence , covariance intersection , covariance , non negative matrix factorization , cluster analysis , computer science , contextual image classification , representation (politics) , covariance matrix , set (abstract data type) , matrix decomposition , image (mathematics) , estimation of covariance matrices , mathematics , algorithm , eigenvalues and eigenvectors , statistics , physics , quantum mechanics , politics , political science , law , programming language
Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.