Abstract
A novel active Affinity Propagation algorithm for pairwise constrained image clustering is proposed. It selects the most informative image pairs and then queries human expert for pairwise must-link and cannot-link constraints between these pairs. The constraints are then used as partial background information to supervise the Affinity Propagation based image clustering resulting in a significant performance improvement. Experimental results on different image datasets show that the proposed approach outperforms baseline and state-of-the-art active clustering approaches.
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Habashi, S., Ismail, M.A., Nagi, M. (2018). Semi-supervised Image Clustering Using Active Affinity Propagation. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_65
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DOI: https://doi.org/10.1007/978-3-319-64861-3_65
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