Abstract
Extracting image feature points and classification methods is the key of content-based image classification. In this paper, SIFT(Scale-invariant feature transform) algorithm is used to extract feature points, all feature points extracted are clustered by K-means clustering algorithm, and then BOW(bag of word) of each image is constructed. Finally, SVM(Support Vector Machine) is used to train a multi-class classifier to classify images. The SIFT algorithm has a strong tolerance for scaling, rotation, brightness changes, and noise. The k-means algorithm is simple in structure and fast in convergence. SVM can get better results in small sample training set and has excellent generalization ability. Experimental results show that the accuracy of image classification in the above methods is about 90%.
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