Name
Abstract | ||
|---|---|---|
In this paper, we propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). To deal with the data points sampled from a nonlinear manifold, for each data point, we construct a local clique comprising this data point and its neighboring data points. Inspired by the Fisher criterion, we use a local discriminant model for each local clique to evaluate the clustering performance of samples within the local clique. To obtain the clustering result, we further propose a unified objective function to globally integrate the local models of all the local cliques. With the unified objective function, spectral relaxation and spectral rotation are used to obtain the binary cluster indicator matrix for all the samples. We show that LDMGI shares a similar objective function with the spectral clustering (SC) algorithms, e.g., normalized cut (NCut). In contrast to NCut in which the Laplacian matrix is directly calculated based upon a Gaussian function, a new Laplacian matrix is learnt in LDMGI by exploiting both manifold structure and local discriminant information. We also prove that K-means and discriminative K-means (DisKmeans) are both special cases of LDMGI. Extensive experiments on several benchmark image datasets demonstrate the effectiveness of LDMGI. We observe in the experiments that LDMGI is more robust to algorithmic parameter, when compared with NCut. Thus, LDMGI is more appealing for the real image clustering applications in which the ground truth is generally not available for tuning algorithmic parameters. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TIP.2010.2049235 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
image clustering,laplacian matrix,gaussian function,clustering result,fisher criterion,local discriminant models,clustering performance,spectral clustering algorithms,binary cluster indicator matrix,pattern clustering,global integration,local discriminant model,local clique,unified objective function,objective function,matrix algebra,k-means clustering,spectral analysis,image sampling,local model,data point,spectral clustering,nonlinear manifold,ldmgi share,clustering,data points sample,local discriminant information,k means,clustering algorithms,principal component analysis,ground truth,manifolds,matrix decomposition,discriminative model,robustness,k means clustering | k-means clustering,Spectral clustering,Fuzzy clustering,Laplacian matrix,Pattern recognition,Clique,Correlation clustering,Artificial intelligence,Cluster analysis,Mathematics,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
19 | 10 | 1941-0042 |
Citations | PageRank | References |
141 | 3.86 | 39 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Yang | 1 | 6873 | 271.72 |
| Dong Xu | 2 | 7616 | 291.96 |
| Feiping Nie | 3 | 7061 | 309.42 |
| Shuicheng Yan | 4 | 767 | 25.71 |
| Yue-Ting Zhuang | 5 | 3549 | 216.06 |