Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse...

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Bibliographic Details
Published in:PloS one 2017-06, Vol.12 (6), p.e0178317-e0178317
Main Authors: Zhang, Shanwen, Wu, Xiaowei, You, Zhuhong
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Biological research
Biology and Life Sciences
Classification
Computer applications
Computer memory
Computer science
Cost engineering
Costs
Data bases
Dictionaries
Ecology
Engineering and Technology
Environmental management
Feasibility
Feasibility studies
Feature extraction
Feature recognition
Flowers & plants
Herbal medicine
Identification
Image Processing, Computer-Assisted
International conferences
Leaves
Mathematical analysis
Mathematical models
Methods
Pattern recognition
Pattern Recognition, Automated - methods
Physical Sciences
Plant extracts
Plant Leaves - anatomy & histology
Plant protection
Plant species
Plant taxonomy
Plants - anatomy & histology
Properties
Research and Analysis Methods
Social Sciences
Species Specificity
Src protein
Training
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Summary:Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0178317