An improved EM algorithm for content based image retrieval

Author

Yu, Mingyan ; Liu, Haiyuan ; Qiu, Yanhang ; Yan, Ying

Author_Institution

Inf. Technol. Dept., Guangdong Commun. Polytech., Guangzhou, China

fYear

2011

fDate

27-29 June 2011

Firstpage

2047

Lastpage

2050

Abstract

Content Based Image Retrieval (CBIR) mainly contains two phases: first, to represent an image; second, to measure the dissimilarity between two images. Expectation-Maximization (EM) is a popular algorithm for clustering Gauss mixtures for the image representation, but the greedy nature of EM make it hard to get an optimal model for CBIR. In this paper , we introduce an improved EM algorithm for clustering Gaussian Mixtures (GMs) to represent an image, instead of regular EM algorithm. We also use different dissimilarity measures for different queries according to their statistics features. Experiments show this approach can greatly improve the performance of CBIR.

Keywords

Gaussian processes; content-based retrieval; expectation-maximisation algorithm; image representation; image retrieval; pattern clustering; EM algorithm; Gauss mixture clustering; content based image retrieval; expectation-maximization algorithm; image dissimilarity measurement; image representation; statistics features; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Image color analysis; Image retrieval; CBIR; EM; EMD; GM;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Service System (CSSS), 2011 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-9762-1

Type

conf

DOI

10.1109/CSSS.2011.5974516

Filename

5974516