The Experts below are selected from a list of 87945 Experts worldwide ranked by ideXlab platform
Davar Giveki - One of the best experts on this subject based on the ideXlab platform.
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Scene classification using a new Radial Basis Function classifier and integrated SIFT–LBP features
Pattern Analysis and Applications, 2020Co-Authors: Davar Giveki, Maryam KaramiAbstract:Scene classification is one of the most significant and challenging tasks in computer vision. This paper presents a new method for scene classification using bag of visual words and a particle swarm optimization (PSO)-based artificial neural network classifier. Contributions of this paper are introducing a novel feature integration method using scale invariant feature transform (SIFT) and local binary pattern (LBP) and a new framework for training Radial Basis Function neural network, combining optimum steepest decent method with a specially designed PSO-based optimizer for center adjustment of Radial Basis Function neural network. Our study shows that using LBP increases the performance of classification task compared to using SIFT only. In addition, our experiments on Proben1 dataset demonstrate improvements in classification performance (averagely about 6.04%) and convergence speed of the proposed Radial Basis Function neural network. The proposed Radial Basis Function neural network is then employed in scene classification task. Results are reported for classification of the Oliva and Torralba, Fei–Fei and Perona and Lazebnik et al. datasets. We compare the performance of the proposed classifier with a multi-way SVM classifier. Experimental results show the superiority of the proposed classifier over the state-of-the-art on the three datasets.
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Scene classification using a new Radial Basis Function classifier and integrated SIFT–LBP features
Pattern Analysis and Applications, 2020Co-Authors: Davar Giveki, Maryam KaramiAbstract:Scene classification is one of the most significant and challenging tasks in computer vision. This paper presents a new method for scene classification using bag of visual words and a particle swarm optimization (PSO)-based artificial neural network classifier. Contributions of this paper are introducing a novel feature integration method using scale invariant feature transform (SIFT) and local binary pattern (LBP) and a new framework for training Radial Basis Function neural network, combining optimum steepest decent method with a specially designed PSO-based optimizer for center adjustment of Radial Basis Function neural network. Our study shows that using LBP increases the performance of classification task compared to using SIFT only. In addition, our experiments on Proben1 dataset demonstrate improvements in classification performance (averagely about 6.04%) and convergence speed of the proposed Radial Basis Function neural network. The proposed Radial Basis Function neural network is then employed in scene classification task. Results are reported for classification of the Oliva and Torralba, Fei–Fei and Perona and Lazebnik et al. datasets. We compare the performance of the proposed classifier with a multi-way SVM classifier. Experimental results show the superiority of the proposed classifier over the state-of-the-art on the three datasets.
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an improved Radial Basis Function neural network for object image retrieval
Neurocomputing, 2015Co-Authors: Gholam Ali Montazer, Davar GivekiAbstract:Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and Function approximation tasks. Hence, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results. This paper presents a new learning method for RBFNNs. An improved algorithm for center adjustment of RBFNNs and a novel algorithm for width determination have been proposed to optimize the efficiency of the Optimum Steepest Decent (OSD) algorithm. To initialize the Radial Basis Function units more accurately, a modified approach based on Particle Swarm Optimization (PSO) is presented. The obtained results show fast convergence speed, better and same network response in fewer train data which states the generalization power of the improved neural network. The Improved PSO-OSD and Three-phased PSO-OSD algorithms have been tested on five benchmark problems and the results have been compared. Finally, using the improved Radial Basis Function neural network we propose a new method for object image retrieval. The images to be retrieved are object images that can be divided into foreground and background. Experimental results show that the proposed method is really promising and achieves high performance.
Meng Xiu-yun - One of the best experts on this subject based on the ideXlab platform.
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Research on MIMU Error Modeling Based on Ridge Regression Radial Basis Function Neuron Network
Computer Simulation, 2010Co-Authors: Meng Xiu-yunAbstract:It is one of the main methods to improve the performance of Strap-down Inertial Navigation System for compensating the random drift and bias of MEMS (Micro Electro Mechanical systems) IMU (Inertial Measurement Unit). In order to eliminate latent multicollinearity of Radial Basis Function neuron network output layer and model the drift and bias of MIMU accurately,the Radial Basis Function neuron network based on ridge regression method was proposed which was applied in modeling and compensating MIMU errors. The simulation shows,compared to the AR model,the precision of compensation of MIMU error using Radial Basis Function neuron network based on ridge regression method is equal to the fourth order AR model,better than first order AR model,and no data stabilization processing.
Noureddine Zerhouni - One of the best experts on this subject based on the ideXlab platform.
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Recurrent Radial Basis Function network for time-series prediction
Engineering Applications of Artificial Intelligence, 2003Co-Authors: Ryad Zemouri, Daniel Racoceanu, Noureddine ZerhouniAbstract:Abstract This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to dynamic monitoring and prognosis. Based on the architecture of the conventional Radial Basis Function networks, the RRBFN have input looped neurons with sigmoid activation Functions. These looped-neurons represent the dynamic memory of the RRBF, and the Gaussian neurons represent the static one. The dynamic memory enables the networks to learn temporal patterns without an input buffer to hold the recent elements of an input sequence. To test the dynamic memory of the network, we have applied the RRBFN in two time series prediction benchmarks (MacKey-Glass and Logistic Map). The third application concerns an industrial prognosis problem. The nonlinear system identification using the Box and Jenkins gas furnace data was used. A two-steps training algorithm is used: the RCE training algorithm for the prototype's parameters, and the multivariate linear regression for the output connection weights. The network is able to predict the two temporal series and gives good results for the nonlinear system identification. The advantage of the proposed RRBF network is to combine the learning flexibility of the RBF network with the dynamic performances of the local recurrence given by the looped-neurons.
Chi F. Fung - One of the best experts on this subject based on the ideXlab platform.
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Recurrent Radial Basis Function networks for adaptive noise cancellation
Neural Networks, 1995Co-Authors: Steve A. Billings, Chi F. FungAbstract:Abstract Radial Basis Function neural network architectures are introduced for the nonlinear adaptive noise cancellation problem. Both FIR and IIR filter designs are considered, and it is shown that by exploiting the duality with system identification, the nonlinear IIR filter can be configured as a recurrent Radial Basis Function network. Details of network training that is based on a combined k-means clustering and Givens routine, the inclusion of linear dynamic network links, and metrics for performance monitoring are also discussed. Examples are included to demonstrate the degree of noise suppression that can be achieved based on the new design.
De-shuang Huang - One of the best experts on this subject based on the ideXlab platform.
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Non-linear cancer classification using a modified Radial Basis Function classification algorithm.
Journal of biomedical science, 2005Co-Authors: Hong-qiang Wang, De-shuang HuangAbstract:This paper proposes a modified Radial Basis Function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the Radial Basis Function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.
