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

Chih Chung Hsu, Chia Wen Lin

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8017517
Pages (from-to)421-429
Number of pages9
JournalIEEE Transactions on Multimedia
Volume20
Issue number2
DOIs
Publication statusPublished - 2018 Feb

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'CNN-Based joint clustering and representation learning with feature drift compensation for large-scale image data'. Together they form a unique fingerprint.

Cite this