Bounded Laplace Mixture Model with Applications to Image Clustering and Content Based Image Retrieval

In this paper, we propose the bounded Laplace mixture model (BLMM). We also propose a new modeling scheme for wavelet coefficients based on BLMM and we apply it to image clustering and content based image retrieval (CBIR). The clustering stage is also performed by BLMM. In the proposed applications, BLMM is applied for feature extraction where each image is decomposed into a set of wavelet subspaces and a two component BLMM is adopted to illustrate the statistical characteristics of the wavelet coefficients for each wavelet subspace. The model parameters adapted from proposed model, reflect the image features of wavelet domain for each subspace and selected to formulate the feature space which is further used in clustering and CBIR. UIUC, KTH-TIPS and DTD databases are considered to demonstrate the viability and effectiveness of proposed algorithm in image clustering and CBIR. From set of experiments, BLMM has demonstrated its effectiveness in modeling the wavelet coefficients in feature extraction, image clustering and CBIR.