Feature synthesized EM algorithm for image retrieval

As a commonly used unsupervised learning algorithm in Content-Based Image Retrieval (CBIR), Expectation-Maximization (EM) algorithm has several limitations, including the curse of dimensionality and the convergence at a local maximum. In this article, we propose a novel learning approach, namely Coevolutionary Feature Synthesized Expectation-Maximization (CFS-EM), to address the above problems. The CFS-EM is a hybrid of coevolutionary genetic programming (CGP) and EM algorithm applied on partially labeled data. CFS-EM is especially suitable for image retrieval because the images can be searched in the synthesized low-dimensional feature space, while a kernel-based method has to make classification computation in the original high-dimensional space. Experiments on real image databases show that CFS-EM outperforms Radial Basis Function Support Vector Machine (RBF-SVM), CGP, Discriminant-EM (D-EM) and Transductive-SVM (TSVM) in the sense of classification performance and it is computationally more efficient than RBF-SVM in the query phase.