Joint Pairwise Learning and Image Clustering Based on a Siamese CNN

Weng Tai Su, Chih Chung Hsu, Ziling Huang, Chia Wen Lin, Gene Cheung

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

How to use a deep convolutional neural network (CNN) to efficiently and effectively learn representations of a large unlabeled set of images and group them into clusters remains a challenging problem. To address this problem, we propose a Siamese clustering CNN (SC-CNN) to iteratively learn discriminative representations for image clustering. Based on the proposed SC-CNN, we employ a mini-batch-based joint pairwise representation learning and clustering scheme to make the computation and storage cost efficient for large-scale image clustering on a personal computer with a commercial GPU graphic card. On top of SC-CNN, the proposed pairwise learning scheme effectively learns discriminative representations by appropriately selecting same-cluster and different-cluster image pairs from the results of each clustering iteration. Experimental results demonstrate that the proposed method outperforms start-of-the-art clustering schemes in clustering accuracy on public image sets.

原文English
主出版物標題2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
發行者IEEE Computer Society
頁面1992-1996
頁數5
ISBN(電子)9781479970612
DOIs
出版狀態Published - 2018 8月 29
事件25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
持續時間: 2018 10月 72018 10月 10

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
ISSN(列印)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
國家/地區Greece
城市Athens
期間18-10-0718-10-10

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦視覺和模式識別
  • 訊號處理

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