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This paper proposes a dynamic-context cooperative quantum-behaved particle swarm optimization algorithm. The proposed algorithm incorporates a new method for dynamically updating the context vector each time it completes a cooperation operation with other particles. We first explain how this leads to enhanced search ability and improved optimization over previous methods, and demonstrate this empirically with comparative experiments using benchmark test functions. We then demonstrate a practical application of the proposed method, by showing how it can be applied to optimize the parameters for Otsu image segmentation for processing medical images. Comparative experimental results show that the proposed method outperforms other state-of-the-art methods from the literature. [ABSTRACT FROM AUTHOR]

Image segmentation is an important and well-known ill-posed inverse problem in computer vision. It is a process of assigning a label to each pixel in a digital image so that pixels with the same label have similar characteristics. Chan–Vese model which belongs to partial differential equation approaches has been widely used in image segmentation tasks. Chan–Vese model has to optimize a non-convex problem. It usually converges to local minima. Furthermore, the length penalty item which is critical to the final results of Chan–Vese model makes the model be sensitive to parameter settings and costly in computation. In order to overcome these drawbacks, a novel bi-convex fuzzy variational image segmentation method is proposed. It is unique in two aspects: (1) introducing fuzzy logic to construct a bi-convex object function in order to simplify the procedure of finding global optima and (2) efficiently combining the length penalty item and the numerical remedy method to get better results and to bring robustness to parameter settings and greatly reduce computation costs. Experiments on synthetic, natural, medical and radar images have visually or quantitatively validated the superiorities of the proposed method compared with five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a novel Dictionary Learning and Sparse Representation-based Classifier (DLSRC) for image segmentation. In DLSRC, instances-based learning is adopted to find representative dictionaries that can sparsely code various classes of prototype samples in images. Then an incremental version of DLSRC, IDLSRC, is advanced for incremental learning of accumulating knowledge obtained from labeled data. The unsupervised clustering algorithm provides initial labeled samples, and then the labels of candidate samples are incrementally predicted by defining a consistency-enhanced evaluation function. Some experiments are taken on both the artificial texture images and real Synthetic Aperture Radar (SAR) images, to investigate the performance of DLSRC and IDLSRC. Some aspects including (1) the comparison of DLSRC with the Sparse Representation based Classifier (SRC) and some unsupervised clustering approaches, (2) the comparison of IDLSRC with DLSRC, are tested, and the results prove the superiority of our proposed method to its counterparts. [Copyright &y& Elsevier]