Image Clustering based on Deep Sparse Representations
文献类型：会议论文
| 作者 | Lv Le1 ; Zhao Dongbin1; Deng QingQiong2
|
| 出版日期 | 2017-02 |
| 会议日期 | 6-9 Dec. 2016 |
| 会议地点 | Athens, Greece |
| DOI | 10.1109/SSCI.2016.7850110 |
| 英文摘要 | Currently, the supervised trained deep neural networks (DNNs) have been successfully applied in several image classification tasks. However, how to extract powerful data representations and discover semantic concepts from unlabeled data is a more practical issue. Unsupervised feature learning methods aim at extracting abstract representations from unlabeled data. Large amount of research works illustrate that these representations can be directly used in the supervised tasks. However, due to the high dimensionality of these representations, it is difficult to discover the categorical concepts among them in an unsupervised way. In this paper, we propose combining the winner-take-all autoencoder with the bipartite graph partitioning algorithm to cluster unlabeled image data. The winner-take-all autoencoder can learn the additive sparse representations. By the experiments, we present the properties of the sparse representations. The bipartite graph partitioning can take full advantage of them and generate semantic clusters. We discover that the confident instances in each cluster are well discriminated. Based on the initial clustering result, we further train a support vector machine (SVM) to refine the clusters. Our method can discover the categorical concepts rapidly and the experiment shows that the clustering performance of our method is good. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14471]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 作者单位 | 1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2.College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China |
| 推荐引用方式 GB/T 7714 | Lv Le,Zhao Dongbin,Deng QingQiong. Image Clustering based on Deep Sparse Representations[C]. 见:. Athens, Greece. 6-9 Dec. 2016. |
入库方式： OAI收割
来源：自动化研究所
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