Robust Affinity Propagation for Picture Clustering

余償鑫

doi:10.6843/NTHU.2010.00485

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Robust Affinity Propagation for Picture Clustering

強健式親和性互動方法在圖像分群上的研究

余償鑫 , Masters　　Advisor：王家祥　　

英文 DOI： 10.6843/NTHU.2010.00485

圖片分群；親和性互動；照片分群；圖像分群； affinity propagation ； picture clustering ； image clustering ； photo clustering

Abstract │ Reference (30) │ Altmetrics

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In recent years, taking pictures with digital camera becomes more and more popular. Unlike traditional photos, the cost of taking a digital photo is nearly free, so users often have a great number of digital pictures. For the purpose of management, those pictures should be well categorized. However, grouping a lot of pictures by hand is a difficult and boring task. As a result, how to use computer to automatically group numerous and chaotic digit pictures efficiently is an important research challenge. In this thesis, we propose a content-based picture clustering method for this topic. The proposed method can be separated into three phases. First, phase I extracts local SIFT (Scale Invariant Feature Transformation) features and global MPEG-7 CLD (Color Layout Descriptor) features from all input pictures. SIFT features can describe distinctive local characteristics of an image excluding the color information. Then, we add the color feature to compensate the problem of SIFT. In phase II, we adopt the Affinity Propagation (AP) algorithm as our image clustering method. Further, we improve the instability by appending an estimating step that can evaluate a more suitable initial setting of AP. Finally, phase III is the post-processing stage that merges those small and similar groups produced in phase II. The experimental results show that the proposed method has over 80% ARI accuracy score for 1000 pictures. When the size of the dataset expands to 3000 pictures, the ARI accuracy score is still 70%. On average, our proposed method has 54% improvement in terms of ARI score, as compared to the pure AP algorithm. All the experimental results show that the proposed clustering algorithm is effective and robust.

Reference ( 30 ) 〈TOP〉

[1]. Y. Jing and S. Baluja, “VisualRank: Applying PageRank to Large-Scale Image Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1877-1890, 2008.
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[4]. D. Cai, X. He, Z. Li, W.Y. Ma, and J.R. Wen, “Hierarchical clustering of WWW image search results using visual, textual and link information,” in Proc. ACM International Conference on Multimedia, pp. 952-959, 2004.
連結：
[5]. T. Liu, C. Rosenberg, and H. A. Rowley, “Clustering Billions of Images with Large Scale Nearest Neighbor Search,” in Proc. IEEE Workshop on Applications of Computer Vision, pp. 28, 2007.
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[6]. B. J. Frey and D. Dueck, “Clustering by passing messages between data points,” Science, Vol. 315, No. 5814, pp. 1136800-976, 2007.
連結：
[7]. B.J. Frey and D. Dueck, “Non-metric affinity propagation for unsupervised image categorization,” in Proc. International Conference on Computer Vision, pp. 1-8, 2007.
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