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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5590
Title: Likelihood learning in modified Dirichlet Process Mixture Model for video analysis
Authors: Kumaran S.K.
Chakravarty A.
Dogra D.P.
Pratim Roy, Partha
Published in: Pattern Recognition Letters
Abstract: Rapid advancement in machine learning has expedited computer vision-based research applicable to traffic analysis. A 2-stage inference process has been proposed in this paper to learn data distributions applicable to object motion modeling and path learning. In the first stage, a posterior probability learning has been used to get the initial clusters. In the subsequent stage, we use an inference method for likelihood learning by introducing a velocity parameter. It decides the speed at which the model converges to obtain the final clusters. A new sampling method has been proposed that performs better as compared to the Gibbs sampling in terms of computation time. The results demonstrate that the technique has relevance in computer vision applications. The proposed method performs better than the state-of-the-art unsupervised learning methods. © 2019 Elsevier B.V.
Citation: Pattern Recognition Letters (2019), 128(): 211-219
URI: https://doi.org/10.1016/j.patrec.2019.09.005
http://repository.iitr.ac.in/handle/123456789/5590
Issue Date: 2019
Publisher: Elsevier B.V.
Keywords: Bayesian inference
Computer vision
Dirichlet Process Mixture Model
Statistical machine learning
Traffic analysis
Unsupervised learning
ISSN: 1678655
Author Scopus IDs: 57209233557
57210996384
35408975400
56880478500
Author Affiliations: Kumaran, S.K., School of Electrical Sciences, Indian Institute of Technology BhubaneswarOdisha 752050, India
Chakravarty, A., School of Electrical Sciences, Indian Institute of Technology BhubaneswarOdisha 752050, India
Dogra, D.P., School of Electrical Sciences, Indian Institute of Technology BhubaneswarOdisha 752050, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology RoorkeeUttarakhand 247667, India
Corresponding Author: Kumaran, S.K.; School of Electrical Sciences, Indian Institute of Technology BhubaneswarIndia; email: sk47@iitbbs.ac.in
Appears in Collections:Journal Publications [CS]

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