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- W2361837304 abstract "For the situation that the visual dictionary capacity is obtained through artificial experiment repeatedly,a method was proposed that calculated the reasonable capacity of the visual dictionary automatically.First,the scale-invariant feature transform(SIFT)local feature descriptors were employed to construct the feature matrix of the scene image data set,whose dimension was reduced by using principal component analysis(PCA).Second,the feature matrix was clustered through using affinity propagation(AP)method to obtain the reasonable capacity of visual dictionary.And the reasonable capacity of visual dictionary was employed to train support vector machine(SVM)and scene type recognition.Finally,the proposed method was compared with the K-means which was the classic method for the acquisition of visual dictionary capacity.Experimenal results show that the proposed method not only improves the accuracy of the scene type recognition,but reduces the running time and increases the computation efficiency." @default.
- W2361837304 created "2016-06-24" @default.
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- W2361837304 date "2014-01-01" @default.
- W2361837304 modified "2023-09-25" @default.
- W2361837304 title "Research on reasonable capacity of visual dictionary obtaining" @default.
- W2361837304 hasPublicationYear "2014" @default.
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