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Intelligent photo clustering with user interaction and distance metric learning
Authors:Meng Wang  Dinghuang Ji
Affiliation:a AKiiRA Media Systems Inc., Palo Alto 94301, USA
b Institute of Computing Technology, Beijing 100190, PR China
c University of Texas at San Antonio, USA
d Microsoft Research Asia, Beijing 100080, PR China
Abstract:Photo clustering is an effective way to organize albums and it is useful in many applications, such as photo browsing and tagging. But automatic photo clustering is not an easy task due to the large variation of photo content. In this paper, we propose an interactive photo clustering paradigm that jointly explores human and computer. In this paradigm, the photo clustering task is semi-automatically accomplished: users are allowed to manually adjust clustering results with different operations, such as splitting clusters, merging clusters and moving photos from one cluster to another. Behind users’ operations, we have a learning engine that keeps updating the distance measurements between photos in an online way, such that better clustering can be performed based on the distance measure. Experimental results on multiple photo albums demonstrated that our approach is able to improve automatic photo clustering results, and by exploring distance metric learning, our method is much more effective than pure manual adjustments of photo clustering.
Keywords:Photo clustering  Distance metric learning  Online learning  Interactive computing
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