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
专题模式识别国家重点实验室_先进数据分析与学习
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
推荐引用方式
GB/T 7714
Jianlong Chang,Lingfeng Wang,Gaofeng Meng,et al. Deep Adaptive Image Clustering[C],2017.
条目包含的文件 下载所有文件
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
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Jianlong Chang]的文章
[Lingfeng Wang]的文章
[Gaofeng Meng]的文章
百度学术
百度学术中相似的文章
[Jianlong Chang]的文章
[Lingfeng Wang]的文章
[Gaofeng Meng]的文章
必应学术
必应学术中相似的文章
[Jianlong Chang]的文章
[Lingfeng Wang]的文章
[Gaofeng Meng]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Deep_Adaptive_Image_ICCV_2017_paper.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。