Photo clustering has been widely explored in many applications such as album management. But automatic
clustering can hardly achieve satisfying performance due to the large variety of photos' content. This paper
proposes a semi-automatic photo clustering scheme that attempts to improve clustering performance with users'
interactions. Users can adjust the results of automatic clustering, and a set of constraints among photos are
generated accordingly. A distance metric is then learned with these constraints and we can re-implement clustering
with this metric. We conduct experiments on different photo albums, and experimental results have
demonstrated that our approach is able to improve automatic photo clustering results, and it is better than pure
manual adjustment approach by exploring distance metric learning.
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