a new scene classification method based on local gabor features

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ID: 167748
2015
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Abstract
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 K-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.
Reference Key
dong2015mathematicala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Baoyu Dong;Guang Ren
Journal journal of power sources
Year 2015
DOI
10.1155/2015/109718
URL
http://dx.doi.org/10.1155/2015/109718
Keywords
civil engineering (general)mathematics engineering (general)

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