Title: | Robust Affinity Propagation using Preference Estimation |
Authors: | Yang, Kai-Chao Yu, Chang-Hsin Wang, Jia-Shung |
Citation: | WSCG '2012: Poster Papers Proceedings: The 20th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in cooperation with EUROGRAPHICS: Plzen, Czech Republic, June 26-28, 2012, p. 11-14. |
Issue Date: | 2012 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | http://wscg.zcu.cz/WSCG2012/!_2012-Posters-proceedings.pdf http://hdl.handle.net/11025/15505 |
ISBN: | 978-80-86943-80-0 |
Keywords: | šíření afinity;klasifikační algoritmy;klastrovací metoda;klasifikace obrazů |
Keywords in different language: | affinity propagation;classification algorithms;clustering method;image classification |
Abstract: | Affinity propagation is a novel unsupervised learning algorithm for exemplar-based clustering without the priori knowledge of the number of clusters (NC). In this article, the influence of the “preference” on the accuracy of AP output is addressed. We present a robust AP clustering method, which estimates what preference value could possibly yield an optimal clustering result. To demonstrate the performance promotion, we apply the robust AP on picture clustering, using local SIFT, global MPEG-7 CLD, and the proposed preference as the input of AP. The experimental results show that over 40% enhancement of ARI accuracy for several image datasets. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG '2012: Poster Paper Proceedings |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/15505
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