Learning-based leaf image recognition frameworks

NCUIR > Engineering College > Departmant of Mechanical Engineering > journal & Dissertation > Item 987654321/108942

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108942

Title:	Learning-based leaf image recognition frameworks
Authors:	林智揚;Hsiao, Jou-Ken;Kang, Li-Wei;Chang, Ching-Long;Lin, Chih-Yang
Contributors:	工學院機械工程學系
Keywords:	Artificial intelligence;Bag-of-words (BoW);Dictionary learning;Leaf image recognition;Plant identification;Sparse representation
Date:	2015-01-01
Issue Date:	2026-04-23 15:16:50 (UTC+8)
Publisher:	Springer Verlag;Switzerland: Springer International Publishing AG
Abstract:	摘要： Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this chapter, we propose two learning-based leaf image recognition frameworks for automatic plant identification and conduct a comparative study between them with existing approaches. First, we propose to learn sparse representation for leaf image recognition. In order to model leaf images, we learn an over-complete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Second, we also propose a general bag-of-words (BoW) model-based recognition system for leaf images, mainly used for comparison. We experimentally compare the two learning-based approaches and show unique characteristics of our sparse representation-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two proposed methods. We also show that the proposed sparse representation-based framework can outperform our BoW-based one and state-of-the-art approaches, conducted on the same dataset. 出版者： Switzerland: Springer International Publishing AG 出版日期： 2015 出處： Intelligent Systems in Science and Information 2014, 2015, Vol.591, p.77-91 資源來源： Springer Books 版權： Springer International Publishing Switzerland 2015 識別號： ISSN: 1860-949X 識別號： ISBN: 9783319146539 識別號： ISBN: 331914653X 識別號： EISSN: 1860-9503 識別號： EISBN: 9783319146546 識別號： EISBN: 3319146548 識別號： DOI: 10.1007/978-3-319-14654-6_5 識別號： OCLC: 958503382 識別號： LCCallNum: Q342Q334-342TJ210.2-
Appears in Collections:	[Departmant of Mechanical Engineering ] journal & Dissertation

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