Content-based image retrieval using a Gaussian mixture model in the wavelet domain

Hua Yuan; Xiao-Ping Zhang; Ling Guan

doi:10.1117/12.503262

23 June 2003 Content-based image retrieval using a Gaussian mixture model in the wavelet domain

Hua Yuan, Xiao-Ping Zhang, Ling Guan

Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003) https://doi.org/10.1117/12.503262
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland

Abstract

The research on Content-based Image Retrieval (CBIR) has been very active in recent years. The performance of a CBIR system can be significantly improved by selecting a good indexing feature space to represent image characteristics. In this paper, we introduce a statistical-model based technique for analyzing and extracting image features in the wavelet domain. The images are decomposed into a set of wavelet subspaces in the wavelet domain and for each wavelet subspace, a two component Gaussian mixture model is developed to describe the statistical characteristics of the wavelet coefficients. The model parameters, which are a good reflection of image features in the wavelet subspaces, are obtained by an EM (Expectation-Maximization) algorithm and employed to construct the indexing feature space for a CBIR system. We apply the new method on the Brodatz image database to demonstrate its performance. The experimental results indicate that our indexing feature space is very effective in representing image characteristics and provides a high retrieval rate in the CBIR system. When compared with some other conventional feature extraction methods, the new method achieves comparable retrieval performance with less number of features in the feature space, which means it is more computationally efficient.

Citation Download Citation

Hua Yuan, Xiao-Ping Zhang, and Ling Guan "Content-based image retrieval using a Gaussian mixture model in the wavelet domain", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); https://doi.org/10.1117/12.503262

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