Perceptual Hash Image Classification Algorithm based on SIFT Feature
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Xi Li, Bin Tian, Nan Xu
Image classification with wide application in computer vision refers to an image processing method which classifies different categories of goals based on different features reflected in image information. BOW-SVM is a typical image classification method with higher accuracy but unsatisfactory operating performance. To improve performance and accuracy more effectively, an efficient image classification method based on HOG-PCA is presented. First, extract and whiten features of histogram of oriented gradients (HOG), then make down sampling randomly to unify scale, and then make feature mapping through principal component analysis (PCA), and finally make nearest neighbor classification through least second norm determination. C++ is adopted in the experiment where extraction is made through OPENCV and Darwin and test is made in the PASCAL 2012 dataset, and the experiment compares accuracy and operating performance of this method and BOW-SVM, proving that the presented method is more efficient and of better operating performance.
HOG, Least Second Norm, PCA, Image Classification, Image Feature.