CNN-Based joint clustering and representation learning with feature drift compensation for large-scale image data

Chih Chung Hsu, Chia Wen Lin

研究成果: Article同行評審

101 引文 斯高帕斯(Scopus)

摘要

Given a large unlabeled set of images, how to efficiently and effectively group them into clusters based on extracted visual representations remains a challenging problem. To address this problem, we propose a convolutional neural network (CNN) to jointly solve clustering and representation learning in an iterative manner. In the proposed method, given an input image set, we first randomly pick k samples and extract their features as initial cluster centroids using the proposed CNN with an initial model pretrained from the ImageNet dataset. Mini-batch k-means is then performed to assign cluster labels to individual input samples for a mini-batch of images randomly sampled from the input image set until all images are processed. Subsequently, the proposed CNN simultaneously updates the parameters of the proposed CNN and the centroids of image clusters iteratively based on stochastic gradient descent. We also propose a feature drift compensation scheme to mitigate the drift error caused by feature mismatch in representation learning. Experimental results demonstrate the proposed method outperforms start-of-The-Art clustering schemes in terms of accuracy and storage complexity on large-scale image sets containing millions of images.

原文English
文章編號8017517
頁(從 - 到)421-429
頁數9
期刊IEEE Transactions on Multimedia
20
發行號2
DOIs
出版狀態Published - 2018 2月

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 媒體技術
  • 電腦科學應用
  • 電氣與電子工程

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