Context-Based Support Vector Machine Using Spatial Autocorrelation Function for Image Classification

Rogério G. NegriEliana Pantaleão

Support vector machine classifiers are widely used in pattern recognition applications. Contextual information can improve the classifier accuracy for image classification. The autocorrelation function can be used to estimate how relevant the neighborhood information is for a pixel classification. Thispaper proposes a support vector machine classifier that uses contextual information of the discriminant function for one-against-all multiclass strategy. The spatial autocorrelation function of the discriminant matrix is used to build a filter mask that will include the contextual information in the classification process.

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