Utilizing venation features for efficient leaf image retrieval

Jin Kyu Park, Een Jun Hwang, Yunyoung Nam

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


Most Content-Based Image Retrieval systems use image features such as textures, colors, and shapes. However, in the case of a leaf image, it is not appropriate to rely on color or texture features only as such features are very similar in most leaves. In this paper, we propose a new and effective leaf image retrieval scheme. In this scheme, we first analyze leaf venation which we use for leaf categorization. We then extract and utilize leaf shape features to find similar leaves from the already categorized group in a leaf database. The venation of a leaf corresponds to the blood vessels in organisms. Leaf venations are represented using points selected by a curvature scale scope corner detection method on the venation image. The selected points are then categorized by calculating the density of feature points using a non-parametric estimation density. We show this technique's effectiveness by performing several experiments on a prototype system.

Original languageEnglish
Pages (from-to)71-82
Number of pages12
JournalJournal of Systems and Software
Issue number1
Publication statusPublished - 2008 Jan


  • CBIR
  • Classification
  • Leaf image retrieval
  • Parzen window
  • Venation

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture


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