NDLI: Dynamic Fuzzy Clustering and Its Application in Motion Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Thanh Minh Nguyen Wu, Q.M.J.
Copyright Year	1993
Abstract	Dynamic textures are common in natural scenes and have recently received great attention in video content analysis. A dynamic fuzzy clustering to automatically segment time-varying characteristics and phenomena is presented in this paper. First, compared with the existing models that assume a common prior distribution, which independently generates the labels, the prior distribution in our model is different for each observation and depends on the labels. In addition, in order to properly account for the neighboring observations during the learning step, we introduce the explicit assumptions of the hidden Markov random field model into the dynamic fuzzy clustering. Second, in order to model the observed dynamic texture data, only grayscale information is taken into consideration of the existing models. We use different visual properties by proposing a new distribution in this paper. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback-Leibler divergence information. Numerical experiments are presented, where the proposed model is tested on various simulated and real dynamic textures.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	1019
Ending Page	1031
Page Count	13
File Size	1373282
File Format	PDF
ISSN	10636706
Volume Number	21
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Linear programming Clustering algorithms Heuristic algorithms Dynamics Covariance matrix Motion segmentation linear dynamical system (LDS) Dynamic fuzzy clustering (DFC) dynamic texture segmentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Control and Systems Engineering Computational Theory and Mathematics

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

An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation

Hidden Markov Model Based Dynamic Texture Classification

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures

Hierarchical unsupervised fuzzy clustering

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

Disclaimer: Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

Linear fuzzy clustering techniques with missing values and their application to local principal component analysis

Dynamic Fuzzy Clustering and Its Application in Motion Segmentation

Similar Documents

An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation

Hidden Markov Model Based Dynamic Texture Classification

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures

Hierarchical unsupervised fuzzy clustering

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

Disclaimer: Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

Linear fuzzy clustering techniques with missing values and their application to local principal component analysis

Dynamic Fuzzy Clustering and Its Application in Motion Segmentation