Abstract
Face clustering is a common feature in face annotation system like intelligent photo albums and photo management systems. But unsupervised clustering algorithms perform poorly and researchers turn to work with constrained clustering algorithms that take the user interactions as constraints. Mostly, the constraints are pairwise constraints in the form of Must-Link or Cannot-Link, which can be easily integrated in spectral clustering algorithm. In this paper, we propose a design of face annotation system that can generate more informative constraints and better use constraints with constrained spectral clustering. And we examine the system in a lab situation dataset and a real-live dataset, of which results demonstrate the effectiveness of our method.
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Acknowledgments
This work was partially sponsored by supported by the NSFC (National Natural Science Foundation of China) under Grant No. 61375031, No. 61573068, No. 61471048, and No.61273217, the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01, This work was also supported by Beijing Nova Program, CCF-Tencent Open Research Fund, and the Program for New Century Excellent Talents in University.
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Han, J., Hu, J., Deng, W. (2016). Constrained Spectral Clustering on Face Annotation System. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_1
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DOI: https://doi.org/10.1007/978-981-10-3002-4_1
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