I have a set of images of various products from different websites. I want to cluster the images based on the product shown in the image. How can I generate a suitable feature vector for an image for this purpose?? I just need to know how to generate a feature vector given an image. I tried NetVLAD, but it is very slow. I would like something that is fast and gives high accuracy for clustering in the scenario I have described. Please help me.

  • $\begingroup$ You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations... $\endgroup$ – Mark.F Jan 8 '19 at 11:42
  • $\begingroup$ There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most). $\endgroup$ – user65516 Jan 9 '19 at 15:06
  • $\begingroup$ Can someone help me with this?? $\endgroup$ – user65516 Jan 10 '19 at 6:25

Feature extraction is basically reducing the amount of resources required to describe a large set of data. Analysis with a large number of variables (like images) generally requires a big amounts of memory and computation power. You can start from the simplest and slowly work your way up (in computational requirements) until you reach your desired accuracy.

Start from:

ORB (oriented FAST, rotated robust independent features) -> SURF (speed up robust feature) -> SIFT (scale invariant feature transform) -> OverFeat (Convolutional Network) -> vgg16 ... (There are many more, any neural network model can be used for this)

  • $\begingroup$ I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features?? $\endgroup$ – user65516 Jan 16 '19 at 15:58

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