Knowledge Commons of Institute of Automation，CAS
| Deep Adaptive Image Clustering | |
Jianlong Chang1,2 ; Lingfeng Wang1; Gaofeng Meng1; Shiming Xiang1; Chunhong Pan1
| |
| 2017 | |
| 会议名称 | IEEE International Conference on Computer Vision |
| 会议日期 | 2017-10-22 |
| 会议地点 | Venice, Italy |
| 摘要 | Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To tackle this problem, we propose Deep Adaptive Clustering (DAC) that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters. In DAC, the similarities are calculated as the cosine distance between label features of images which are generated by a deep convolutional network (ConvNet). By introducing a constraint into DAC, the learned label features tend to be one-hot vectors that can be utilized for clustering images. The main challenge is that the ground-truth similarities are unknown in image clustering. We handle this issue by presenting an alternating iterative Adaptive Learning algorithm where each iteration alternately selects labeled samples and trains the ConvNet. Conclusively, images are automatically clustered based on the label features. Experimental results show that DAC achieves state-of-the-art performance on five popular datasets, e.g., yielding 97.75% clustering accuracy on MNIST, 52.18% on CIFAR-10 and 46.99% on STL-10. |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/20361 |
| 专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
| 作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Computer and Control Engineering, University of Chinese Academy of Sciences |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Jianlong Chang,Lingfeng Wang,Gaofeng Meng,et al. Deep Adaptive Image Clustering[C],2017. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Deep_Adaptive_Image_（10707KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
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