A similarity-based leaf image retrieval scheme: Joining shape and venation features

Yunyoung Nam, Eenjun Hwang, Dongyoon Kim

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


In this paper, we propose a new scheme for similarity-based leaf image retrieval. For the effective measurement of leaf similarity, we have considered shape and venation features together. In the shape domain, we construct a matrix of interest points to model the similarity between two leaf images. In order to improve the retrieval performance, we implemented an adaptive grid-based matching algorithm. Based on the Nearest Neighbor (NN) search scheme, this algorithm computes a minimum weight from the constructed matrix and uses it as similarity degree between two leaf images. This reduces necessary search space for matching. In the venation domain, we construct an adjacency matrix from the intersection and end points of a venation to model similarity between two leaf images. Based on these features, we implemented a prototype mobile leaf image retrieval system and carried out various experiments for a database with 1,032 leaf images. Experimental result shows that our scheme achieves a great performance enhancement compared to other existing methods.

Original languageEnglish
Pages (from-to)245-259
Number of pages15
JournalComputer Vision and Image Understanding
Issue number2
Publication statusPublished - 2008 May


  • Leaf image retrieval
  • MPP
  • Mobile computing
  • Shape-based retrieval
  • Similarity-based image retrieval
  • Venation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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