Title
Online discriminative dictionary learning via label information for multi task object tracking
Abstract
In this paper, a supervised approach to online learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a compact and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multi-classifier simultaneously. Combined with multi task sparse learning, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between multi task sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy and robustness.
Year
DOI
Venue
2014
10.1109/ICME.2014.6890250
ICME
Keywords
Field
DocType
image representation,dictionary learning process,image coding,multitask sparse learning,learning (artificial intelligence),label information,dictionaries,discriminative dictionary learning,ideal-code regularization,sparse coding,image classification,online discriminative dictionary learning,multi task learning,object tracking,image sequences,total objective function minimization,dictionary updating,multitask object tracking,encoding,robustness,learning artificial intelligence,particle filters
K-SVD,Computer science,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Classifier (linguistics),Discriminative model,Computer vision,Multi-task learning,Pattern recognition,Neural coding,Video tracking,Machine learning
Conference
ISSN
Citations 
PageRank 
1945-7871
1
0.36
References 
Authors
13
4
Name
Order
Citations
PageRank
Baojie Fan14110.48
Yingkui Du2177.23
Hao Gao3338.86
Baoyun Wang410.36