Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier

Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier
دوره: 
۴۵
شماره: 
۱۳۵
شماره صفحه (از - تا): 
۱-۲۴
چکیده: 

This article approaches scene classification problem by proposing an enhanced bag of features (BoF) model and a modified radial basis function neural network (RBFNN) classifier.‎ The proposed BoF model integrates the image features extracted by histogram of oriented gradients, local binary pattern and wavelet coefficients.‎ The extracted features are obtained in a hierarchical multi-resolution manner.‎ The proposed approach is able to capture multi-level (the pixel-, patch-, and image-level) features.‎ The histograms of features constructed by BoF model are then used for training a modified RBFNN classifier.‎ As a modification, we propose using a new variant of particle swarm optimization, in which the parameters are updated adaptively, for determining the center of Gaussian functions in RBFNN.‎ Experimental results demonstrate that our proposed approach significantly outperforms the state-of-the-art methods on scene classification of OT, FP, and LSP benchmark datasets.‎

استناد: 

Gholam Ali Montazer, and Davar Giveki.‎ 2017.‎ Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier.‎ Neural Processing Letters 45 (135)‎: 1-24.‎

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