NDLI: Model-based motion clustering using boosted mixture modeling

Content Provider	IEEE Xplore Digital Library
Author	Pavlovic, V.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA (Pavlovic, V.)
Abstract	Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with different statistical properties. We propose a general framework for boosted modeling of mixtures of parametric densities. A density is represented as a parametric mixture of kernels where mixture components are now being added recursively, one at a time, until a best fit to data occurs and an optimal number of mixture components is selected. Optimal ML and MAP solutions to this problem are found using the functional gradient techniques. Unlike traditional mixture modeling techniques, the new method does not rely on random parameter initialization and exhaustive exploration of varying model orders (such as the number of mixture components.) The method justifies parameter estimation of new mixture components independently of that of the rest of the mixture, thus allowing tractable use of complex kernels such as HMMs or switching linear dynamic models. The relationship to traditional parametric EM-based mixture modeling algorithms is established We demonstrate the utility of the new algorithm on the problem of discovering motion sequence clusters. Our generative modeling framework has an important advantage over nonparametric approaches in that it can be used for classification as well as synthesis of the learned motion categories.
Sponsorship	IEEE Comput. Soc
File Size	507782
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Character generation Hidden Markov models Clustering algorithms Recursive estimation Computer science Density functional theory Parameter estimation Motion analysis Pattern analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Mixture clustering using multidimensional histograms for skin detection

AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition

Improvements in hidden Markov model based Arabic OCR

Scene Segmentation and Categorization Using NCuts

Introducing termination probabilities to HMM

Visual keyword recognition using hidden Markov models

Modeling inverse covariance matrices by basis expansion

2-D shape classification using hidden Markov model

Application of semiparametric density estimation to classification

Model-based motion clustering using boosted mixture modeling

Similar Documents

Mixture clustering using multidimensional histograms for skin detection

AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition

Improvements in hidden Markov model based Arabic OCR

Scene Segmentation and Categorization Using NCuts

Introducing termination probabilities to HMM

Visual keyword recognition using hidden Markov models

Modeling inverse covariance matrices by basis expansion

2-D shape classification using hidden Markov model

Application of semiparametric density estimation to classification

Model-based motion clustering using boosted mixture modeling