Image retrieval based on Gaussian Mixture Model

This paper presents some explorations and studies on image retrieval. Firstly, RGB color space is converted to HSV color space for feature extraction. Then, the texture features are obtained by using wavelet, which are combined with some color features based on wavelet transform. Finally, the multi-features generated by Gaussian Mixture Model (GMM) are employed to an image retrieval algorithm. The experimental results on an image database show the effectiveness and competitive performance of the GMM-based image retrieval algorithm.

[1]  Raimondo Schettini,et al.  Color for Image Indexing and Retrieval , 2004, Comput. Vis. Image Underst..

[2]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Gustavo Carneiro,et al.  Supervised Learning of Semantic Classes for Image Annotation and Retrieval , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Xiaoying Tai,et al.  Image Retrieval Based on Color and Texture , 2006, 2006 Fifth Mexican International Conference on Artificial Intelligence.

[5]  Tat-Seng Chua,et al.  A semi-naïve Bayesian method incorporating clustering with pair-wise constraints for auto image annotation , 2004, MULTIMEDIA '04.