I'm a beginner in computer vision. I want to know which structure among the following two can get better accuracy of image classification.

  • Structure 1: SIFT feature + SVM
  • Structure 2: bag-of-word feature + SVM

Here's a reference: https://www.mathworks.com/help/vision/ug/image-classification-with-bag-of-visual-words.html.


You want to say bag-of-visual words not bag-of-words, this technique was classicly used in Computer vision before the introduction of neural networks or some more advanced classical tehcniques such us VLAD or Fisher Vectors, in any case it is a good tehcnique to use, but it is not the state-of-the-art today, and I won't recommand you to use for a real life project.

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  • $\begingroup$ Thanks for your advice! As I understand it, the BoW first apply knn to create a histogram vector and then apply svm to classify. I'm not sure such a technique is called "vector quantization" or "two stage classification". And I'm curious about that if such a technique improves the accuracy of classification. $\endgroup$ – Jimmy116 Feb 10 at 8:44

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