A model for image patch-based algorithms

Author

Ni, Karl S. ; Nguyen, Truong Q.

Author_Institution

ECE Dept., UCSD, La Jolla, CA

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

2588

Lastpage

2591

Abstract

An empirical study of the domain of patch-based learning algorithms for image and video processing is conducted. As patch-based algorithms are commonly used, knowledge of the properties of fixed size image patches would prove particularly useful and interesting. We are concerned with investigating the overall distribution of vectorized patches of general images. A multivariate distribution model is proposed and analyzed using various techniques, which include univariate histograms and modified k-nearest neighbors. The model is verified and an application using the distribution model is introduced and compared.

Keywords

image processing; statistical analysis; image patch-based algorithm; image processing; k-nearest neighbor; multivariate distribution model; patch-based learning algorithm; univariate histogram; video processing; Data preprocessing; Discrete cosine transforms; Histograms; Image coding; Interpolation; Parameter estimation; Pixel; Statistical distributions; Statistics; Video compression; Gaussian Mixture Model; distribution; image patch; image properties; multivariate Laplace;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on

Conference_Location

San Diego, CA

ISSN

1522-4880

Print_ISBN

978-1-4244-1765-0

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2008.4712323

Filename

4712323