Utilizing venation features for efficient leaf image retrieval

  • Authors:

  • JinKyu Park;EenJun Hwang;Yunyoung Nam

  • Affiliations:

  • School of Electrical Engineering, Korea University, Anam-dong, Seongbuk-Gu, Seoul, Republic of Korea;School of Electrical Engineering, Korea University, Anam-dong, Seongbuk-Gu, Seoul, Republic of Korea;Graduate School of Information and Communication, Ajou University, Wonchon-dong, Youngtong-Gu, Suwon, Kyunggi-Do, Republic of Korea

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2008

Quantified Score

Hi-index 0.01

Visualization

Abstract

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.