A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.

In this paper, a method for designing of Incremental Granular Model (IGM) based on integration of Linear Regression (LR) and Linguistic Model (LM) with the aid of fuzzy granulation is proposed. Here, IGM is designed by the use of information granulation realized via Context-based Interval Type-2 Fuzzy C-Means (CIT2FCM) clustering. This clustering approach are used not only to estimate the cluster centers by preserving the homogeneity between the clustered patterns from linguistic contexts produced in the output space, but also deal with the uncertainty associated with fuzzification factor. Furthermore, IGM is developed by construction of a LR as a global model, refine it through the local fuzzy if-then rules that capture more localized nonlinearities of the system by LM. The experimental results on two examples reveal that the proposed method shows a good performance in comparison with the previous works.

To overcome the target-aspect sensitivity in radar high resolution range profile (HRRP) recognition, a novel method called Improved Kernel Distance Fuzzy C-means Clustering Method (IKDFCM) is proposed in this paper, which introduces kernel function into fuzzy c-means clustering and relaxes the constraint in the membership matrix. The new method finds the underlying geometric structure information hiding in HRRP target and uses it to overcome the HRRP target-aspect sensitivity. The relaxing of constraint in the membership matrix improves anti-noise performance and robustness of the algorithm. Finally, experiments on three kinds of ground HRRP target under different SNRs and four UCI datasets demonstrate the proposed method not only has better recognition accuracy but also more robust than the other three comparison methods.

In this paper, we propose a method for designing genetically optimized Linguistic Models (LM) with the aid of fuzzy granulation. The fundamental idea of LM introduced by Pedrycz is followed and their design framework based on Genetic Algorithm (GA) is enhanced. A LM is designed by the use of information granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules represented as a fuzzy set. However, it is difficult to optimize the number of linguistic contexts, the number of clusters generated by each context, and the weighting exponent. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on GA. Experiments on the coagulant dosing process in a water purification plant reveal that the proposed method shows better performance than the previous works and LM itself.

This paper studies the design of Cascade Granular Neural Networks (CGNN) for human-centric systems. In contrast to typical rule-based systems encountered in fuzzy modeling, the proposed method consists of two-phase development for CGNN. First, we construct a Granular Neural Network (GNN) which could be treated as a preliminary design. Next, all modeling discrepancies are compensated by a second GNN with a collection of rules that become attached to the regions of the input space where the error is localized. These granular networks are constructed by building a collection of user-centric information granules through Context-based Fuzzy c-Means (CFCM) clustering. Finally, the experimental results on two examples reveal that the proposed approach shows good performance in comparison with the previous works.

In this letter, we propose an improved anchor shot detection (ASD) method in order to effectively retrieve anchor shots from news video. The face location and dissimilarity of icon region are used to reduce false alarms in the proposed method. According to the results of the experiment on several types of news video, the proposed method obtained high anchor detection results compared with previous methods.

This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of the Helmholtz free energy for fuzzy c-means clustering is searched by DA. Numerical experiments are performed and the obtained results indicate that this combinatorial algorithm of SA and DA can represent various cluster shapes and divide data more properly and stably than the standard single DA algorithm.

Image segmentation is an essential processing step for many image analysis applications. In this paper, a novel image segmentation algorithm using fuzzy C-means clustering (FCM) with spatial constraints based on Markov random field (MRF) via Bayesian theory is proposed. Due to disregard of spatial constraint information, the FCM algorithm fails to segment images corrupted by noise. In order to improve the robustness of FCM to noise, a powerful model for the membership functions that incorporates local correlation is given by MRF defined through a Gibbs function. Then spatial information is incorporated into the FCM by Bayesian theory. Therefore, the proposed algorithm has both the advantages of the FCM and MRF, and is robust to noise. Experimental results on the synthetic and real-world images are given to demonstrate the robustness and validity of the proposed algorithm.

Most scheduling applications have been classified into NP-complete problems. This fact implies that an optimal solution for a large scheduling problem is extremely time-consuming. A number of schemes are introduced to solve NP-complete scheduling applications, such as linear programming, neural network, and fuzzy logic. In this paper, we demonstrate a new approach, fuzzy Hopfield neural network, to solve the scheduling problems. This fuzzy Hopfield neural network approach integrates fuzzy c-means clustering strategies into a Hopfield neural network. In this investigation, we utilizes this new approach to demonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration, limited resources and constrained times (execution time and deadline). In the approach, the process and processor of the scheduling problem can be regarded as a data sample and a cluster, respectively. Then, an appropriate Lyapunov energy function is derived correspondingly. The scheduling results can be obtained using a fuzzy Hopfield neural network clustering technique by iteratively updating fuzzy state until the energy function gets minimized. To validate our approach, three scheduling cases for different initial neuron states as well as fuzzification parameters are taken as testbed. Simulation results reveal that imposing the fuzzy Hopfield neural network on the proposed energy function provides a sound approach in solving this class of scheduling problems.