Retrieval of Images Using Mean-Shift and Gaussian Mixtures Based on Weighted Color Histograms

Author

Bouker, Mohamed Ali ; Hervet, Eric

Author_Institution

Comput. Sci. Dept., Univ. of Moncton, Moncton, NB, Canada

fYear

2011

fDate

Nov. 28 2011-Dec. 1 2011

Firstpage

218

Lastpage

222

Abstract

The topic of this paper is Content-Based Image Retrieval (CBIR) based on colors as a content image descriptor. The tool we developed to that purpose modelizes the colors of an image as a set of 2D Gaussian distributions based on weighted color histograms. Then, given a reference image proposed by a user, the system can automatically classify the image and provide the user with the most similar images to the reference image in its category. Experiments with Corel-1000 dataset demonstrate that our method outperforms the known implementations.

Keywords

Gaussian distribution; content-based retrieval; image classification; image colour analysis; image retrieval; 2D Gaussian distributions; Corel-1000 dataset; Gaussian mixtures; content image descriptor; content-based image retrieval; image classification; mean-shift; reference image; weighted color histograms; Dinosaurs; Histograms; Image color analysis; Image retrieval; Indexing; Kernel; Classification; Color Histograms; Content-Based Image Retrieval; Gaussian Mixtures; Mean-Shift;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal-Image Technology and Internet-Based Systems (SITIS), 2011 Seventh International Conference on

Conference_Location

Dijon

Print_ISBN

978-1-4673-0431-3

Type

conf

DOI

10.1109/SITIS.2011.75

Filename

6120653