This weekend I was trying to find a new way to cluster the materials. And I tried to use VLfeat. By looking into the algorithms of VLfeat, I found that k-means might be a good way to cluster it. So I just used the built-in kmeans function in matlab to recognize the materials. Below is the result I got.
As in the original method, I divided each picture(13 pictures including 3 deformed pictures) into training data and testing data. And try to classify all 26 samples into 10 centers.
This result shows the clustering results of the training and testing data:
This result shows the distance between every center and every points.(Each line represents a center)
This result shows the differences of the clustering results of the training and testing data.
This time it gets confused when dealing with butter and denim. Again, I'm pretty sure that it can be solved by adding the factor of variance. And also the failure of recognizing deformed pictures reminds me that last time I just rectified the position of the peaks for every picture, I still need to rectify all the peaks within one picture(split the picture in small columns and rectify every column). So that deformed pictures can be rectified as regular pictures.
So above are my next two steps.
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