Multiple disjoint dictionaries for representation of histopathology images

Shujin Zhu, Yuehua Li, Shivam Kalra, H. R. Tizhoosh

Research output: Contribution to journalArticlepeer-review


With the availability of whole-slide imaging in pathology, high-resolution images offer a more convenient disease observation but also require content-based retrieval of large scans. The bag-of-visual-words methodology has shown a high ability to describe the image content for recognition and retrieval purposes. In this work, a variant of the bag-of-visual-words with multiple dictionaries for histopathology image classification is proposed and tested on the image dataset Kimia Path24 with more than 27,000 patches of size 1000 × 1000 belonging to 24 different classes. Features are extracted from patches and clustered to form multiple codebooks. The histogram intersection approach and support vector machines are exploited to build multiple classifiers. At last, the majority voting determines the final classification for each patch. The experiments demonstrate the superiority of the proposed method for histopathology images that surpasses deep networks, LBP and other BoW results.

Original languageEnglish (US)
Pages (from-to)243-252
Number of pages10
JournalJournal of Visual Communication and Image Representation
StatePublished - Aug 2018


  • Bag-of-words
  • Deep learning
  • Dictionary learning
  • Histopathology
  • Image representation
  • Image retrieval
  • LBP
  • SVM
  • Wholeslide imaging

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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