CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Deep Adaptive Image Clustering
Jianlong Chang1,2; Lingfeng Wang1; Gaofeng Meng1; Shiming Xiang1; Chunhong Pan1
Conference NameIEEE International Conference on Computer Vision
Conference Date2017-10-22
Conference PlaceVenice, Italy
AbstractImage 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,
., yielding 97.75% clustering accuracy on MNIST, 52.18% on CIFAR-10 and 46.99% on STL-10.
Document Type会议论文
Affiliation1.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
Recommended Citation
GB/T 7714
Jianlong Chang,Lingfeng Wang,Gaofeng Meng,et al. Deep Adaptive Image Clustering[C],2017.
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