Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
The ordered weighted averaging operators: theory and applications
The ordered weighted averaging operators: theory and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximating clique and biclique problems
Journal of Algorithms
On bipartite and multipartite clique problems
Journal of Algorithms
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Gait as a Biometric, via Phase-weighted Magnitude Spectra
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
On the Relationship of Human Walking and Running: Automatic Person Identification by Gait
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Analysis
Gait analysis for human identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Ortholog clustering on a multipartite graph
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Combinatorial optimization in system configuration design
Automation and Remote Control
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In many supervised learning problems, objects are represented as a sequence of observations. To classify such data, existing methods build classifiers either based on their dynamics, or the statistics of the observations. However, similar observations shared by most objects are uninformative for identification. In this paper, we present a new approach that identifies similar observations across objects and use only informative data for classification. To do this, we construct a weighted multipartite graph from the training data, with weights representing the similarities between observations from different objects. Identification of uninformative observations is modeled as clustering on this multipartite graph using a combinatorial optimization formulation. Two-level hierarchical classifiers are, then, built using the clustering results. The first layer of the classifiers associates the test observations with a certain cluster, whereas the second level identifies the object within the cluster. Data associated with uninformative clusters are screened out. Final identification for the group of observations is obtained using the majority voting rule only from the informative observations. We apply our algorithm to the gait recognition problem. The hierarchical classifiers are built in four different feature spaces for silhouette images. Final classification is determined by aggregating results from these four feature spaces. The experimental results show that our method results in improved recognition rates in most cases compared with other previously reported methods.