This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the origi...This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the original image,the proposed method first segments the input image into superpixels and,then,several different types of features are calculated according to these superpixels.To increase classification accuracy,the dimensions of these features are reduced using the principal component analysis(PCA)algorithm followed by a weighted serial feature fusion strategy.After constructing a coding dictionary using the nonnegative matrix factorization(NMF)algorithm,the input image is recognized by a support vector machine(SVM)model.The effectiveness of the proposed method was tested on the public Scene-15,Caltech-101,and Caltech-256 datasets,and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.展开更多
基金the National Key Research and Development Program of China under Grant No.2018AAA0103203.
文摘This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the original image,the proposed method first segments the input image into superpixels and,then,several different types of features are calculated according to these superpixels.To increase classification accuracy,the dimensions of these features are reduced using the principal component analysis(PCA)algorithm followed by a weighted serial feature fusion strategy.After constructing a coding dictionary using the nonnegative matrix factorization(NMF)algorithm,the input image is recognized by a support vector machine(SVM)model.The effectiveness of the proposed method was tested on the public Scene-15,Caltech-101,and Caltech-256 datasets,and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.
