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| Content Provider | ACM Digital Library |
|---|---|
| Author | Wang, Huan Tang, Xiaoou Yan, Shuicheng Huang, Thomas |
| Abstract | In this paper, we present a novel spectral analysis algorithm for image clustering. First, the image manifold is embedded onto a low-dimensional feature space with dual objectives, i.e., maximizing the distances of faraway sample pairs meanwhile preserving the local manifold structure, which essentially results in a Trace Ratio optimization problem. Then an efficient iterative procedure is proposed to directly optimize the trace ratio and finally the clustering process is implemented on the derived low-dimensional embedding. Moreover, the linear approximation is also presented for handling the out-of-sample data. Experimental results show that our algorithm, referred to as Maximum Unfolded Embedding, brings an encouraging improvement in clustering accuracy over the state-of-the-art algorithms, such as K-Means, PCA-Kmeans, normalized cut \cite shi00normalized, and Locality Preserving Clustering [13]. |
| Starting Page | 45 |
| Ending Page | 48 |
| Page Count | 4 |
| File Format | |
| ISBN | 1595934472 |
| DOI | 10.1145/1180639.1180656 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2006-10-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Image clustering Maximum unfolded embedding Spectral analysis |
| Content Type | Text |
| Resource Type | Article |
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