NDLI: Leaf Shape Descriptor for Tree Species Identification

Content Provider	IEEE Xplore Digital Library
Author	Yahiaoui, I. Mzoughi, O. Boujemaa, N.
Copyright Year	2012
Abstract	The problem of automatic leaf identification is particularly challenging because, in addition to constraints derived from image processing such as geometric deformations (rotation, scale, translation) and illumination variations, it involves difficulties arising from foliar properties. These include two main aspects: the first is the enormous number and diversity of leaf species and the second, which is relevant to some special species, is the high inter-species and the low intra-species similarity. In this paper, we present a novel boundary-based approach that attempts to overcome the most of these constraints. This method has been compared to results obtained in the image CLEF 2011 plant identification task. The main advantage of this first benchmark edition is that different image retrieval techniques were tested and a crowd-sourced leaf dataset was used. Our method provides the best classification rate for scan and scan-like pictures. Besides its high accuracy, our method satisfies real-time requirements with a low computational cost.
Starting Page	254
Ending Page	259
File Size	537557
Page Count	6
File Format	PDF
ISBN	9781467316590
ISSN	19457871
DOI	10.1109/ICME.2012.130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Histograms Random variables Context Standards Training Vegetation leaf form Plant recognition leaf database feature extraction shape descriptor
Content Type	Text
Resource Type	Article

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