Statistical Modeling in the Wavelet Domain for Compact Feature Extraction and Similarity Measure of Images

Image feature extraction and similarity measure in feature space are active research topics. They are basic components in a content-based image retrieval (CBIR) system. In this letter, we present a new statistical model-based image feature extraction method in the wavelet domain and a novel Kullback divergence-based similarity measure. First, a Gaussian mixture model (GMM) and a more systematic generalized Gaussian mixture model (GGMM) are employed to describe the statistical characteristics of the wavelet coefficients and the model parameters are employed to construct a compact image feature space. A nontrivial expectation-maximization (EM) algorithm for the GGMM model is derived. Subsequently, a new Kullback divergence-based similarity measure with low-computation cost is derived and analyzed. The Brodatz texture image database and some other image databases are used to evaluate the retrieval performance based on the presented new methods. Experimental results indicate that the GMM and the GGMM-based image texture features are very effective in representing multiscale image characteristics and that the new methods outperforms other conventional wavelet-based methods in retrieval performance with a comparable level of computational complexity. It is also demonstrated that for image features extracted by the new statistical models, the similarity measure based on Kullback divergence is more effective than conventional similarity measures.