python - sklearn.cluster.DBSCAN gives unexpected result -


i'm using dbscan method clustering images, gives unexpected result. let's assume have 10 images.

firstly, read images in loop using cv2.imread. compute structural similarity index between each images. after that, have matrix this:

[ [ 1.         -0.00893619  0.          0.          0.          0.50148778      0.47921832  0.          0.          0.        ]  [-0.00893619  1.          0.          0.          0.          0.00996088     -0.01873205  0.          0.          0.        ]  [ 0.          0.          1.          0.57884212  0.          0.             0.         0.          0.          0.        ]  [ 0.          0.          0.57884212  1.          0.          0.              0.         0.          0.          0.        ]  [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]  [ 0.50148778  0.00996088  0.          0.          0.          1.          0.63224396  0.          0.          0.        ]  [ 0.47921832 -0.01873205  0.          0.          0.          0.63224396      1.          0.          0.          0.        ]  [ 0.          0.          0.          0.          0.          0.          0.  1.          0.77507487  0.69697053]  [ 0.          0.          0.          0.          0.          0.          0.  0.77507487  1.          0.74861881]  [ 0.          0.          0.          0.          0.          0.          0.  0.69697053  0.74861881  1.        ]]

looks good. have simple invokation of dbscan:

db = dbscan(eps=0.4, min_samples=3, metric='precomputed').fit(distances) labels = db.labels_ n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

and result is

[0 0 0 0 0 0 0 0 0 0]

what do wrong? why puts images 1 cluster?

dbscan assumes dissimilarity (distance) not similarity. can implemented similarity threshold, (see generalized dbscan)


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