SIAM Journal on Scientific and Statistical Computing
Accelerating EM for Large Databases
Machine Learning
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture synthesis via a noncausal nonparametric multiscale Markov random field
IEEE Transactions on Image Processing
A fast nonparametric noncausal MRF-based texture synthesis scheme using a novel FKDE algorithm
IEEE Transactions on Image Processing
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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.