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Mathematical Problems in Engineering
Volume 2015, Article ID 109718, 14 pages
Research Article

A New Scene Classification Method Based on Local Gabor Features

1College of Electric Information, Dalian Jiaotong University, Dalian 116028, China
2Marine Engineering College, Dalian Maritime University, Dalian 116026, China

Received 10 November 2014; Revised 9 March 2015; Accepted 6 April 2015

Academic Editor: Lucian Busoniu

Copyright © 2015 Baoyu Dong and Guang Ren. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained by -means clustering method and visual word statistics. Second, in order to decrease classification time and improve accuracy, an improved kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of pyramid histogram of visual words (PHOW). The principal components with the bigger interclass separability are retained in feature vectors, which are used for scene classification by the linear support vector machine (SVM) method. The proposed method is evaluated on three commonly used scene datasets. Experimental results demonstrate the effectiveness of the method.