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

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Abstract

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.

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Acknowledgements

The authors are grateful to the anonymous reviewers for the insightful comments and constructive suggestions. Part of this research has been funded by Iranian Research Institute for Information Science and Technology (IranDoc) (No. TMU92-03-44).

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Authors and Affiliations

  1. Information Technology Engineering Department, School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran

    Gholam Ali Montazer

  2. Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran

    Gholam Ali Montazer & Davar Giveki

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  1. Gholam Ali Montazer
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  2. Davar Giveki
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Correspondence to Gholam Ali Montazer.

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Montazer, G.A., Giveki, D. Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier. Neural Process Lett 46, 681–704 (2017). https://doi.org/10.1007/s11063-017-9614-6

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