arxivst stuff from arxiv that you should probably bookmark

Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

Abstract · Mar 23, 2017 09:49 ·

cs-cv cs-lg

Arxiv Abstract

  • Fengfu Li
  • Hong Qiao
  • Bo Zhang
  • Xuanyang Xi

Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft $k$-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance.

Read the paper (pdf) »