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      Fast Estimation of Nonparametric Kernel Density Through PDDP, and its Application in Texture Synthesis

      proceedings-article
      ,
      Visions of Computer Science - BCS International Academic Conference (VOCS)
      BCS International Academic Conference
      22 - 24 September 2008
      Nonparametric density estimation, Kernel density estimation, Principal Component Analysis, Vector quantization, Texture synthesis, Markov random field
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            Abstract

            In thiswork, a newalgorithmis proposed for fast estimation of nonparametricmultivariate kernel density, based on principal direction divisive partitioning (PDDP) of the data space.The goal of the proposed algorithm is to use the finite support property of kernels for fast estimation of density. Compared to earlier approaches, this work explains the need of using boundaries (for partitioning the space) instead of centroids (used in earlier approaches), for better unsupervised nature (less user incorporation), and lesser (or atleast same) computational complexity. In earlier approaches, the finite support of a fixed kernel varies within the space due to the use of cluster centroids. It has been argued that if one uses boundaries (for partitioning) rather than centroids, the finite support of a fixed kernel does not change for a constant precision error. This fact introduces better unsupervision within the estimation framework. Themain contributionof thiswork is the insight gained in the kernel density estimation with the incorporation of clustering algortihm and its application in texture synthesis. Texture synthesis through nonparametric, noncausal, Markov random field (MRF), has been implemented earlier through estimation of and sampling from nonparametric conditional density. The incorporation of the proposed kernel density estimation algorithm within the earlier texture synthesis algorithm reduces the computational complexity with perceptually same results. These results provide the efficacy of the proposed algorithm within the context of natural texture synthesis.

            Content

            Author and article information

            Contributors
            Conference
            September 2008
            September 2008
            : 225-236
            Affiliations
            [0001]Department of Electrical Engineering

            Indian Institute of Technology Kanpur

            Kapur-208016, U.P., India
            Article
            10.14236/ewic/VOCS2008.19
            12fd682c-c7a5-4c29-9d56-4e22305e9443
            © Arnab Sinha et al. Published by BCS Learning and Development Ltd. Visions of Computer Science - BCS International Academic Conference

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Visions of Computer Science - BCS International Academic Conference
            VOCS
            Imperial College, London, UK
            22 - 24 September 2008
            Electronic Workshops in Computing (eWiC)
            BCS International Academic Conference
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/VOCS2008.19
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Nonparametric density estimation,Kernel density estimation,Principal Component Analysis,Vector quantization,Texture synthesis,Markov random field

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