UM
Joint distances by sparse representation and locality-constrained dictionary learning for robust leaf recognition
Zeng, Shaoning; Zhang, Bob; Du, Yong
2017-11
Source PublicationCOMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN0168-1699
Volume142Pages:563-571
AbstractPlant species recognition has been a difficult and important task in agriculture, where computer techniques like image processing and pattern recognition can commendably facilitate plant recognition based on leaf images. The locality-constrained models produced by sparse representation and dictionary learning are a few of the prevailing feature models for leaf image recognition. Previous studies demonstrated that sparsity in representation plays an important role in the recognition, while sparsity constraints are the keys to solve the dictionary learning problems. Many of them focused on improving the sparsity, which is hard, but using large atoms in dictionary learning for high accuracy consumed more training time. Actually, sparse representation and dictionary learning are both based on distance calculation, e.g., Euclidean distance, which is also an aspect possible to obtain an improvement. On the premise of unchanged sparsity, this paper proposed a novel distance based method fusing Sparse Representation and Locality-Constrained Dictionary Learning (SRLC-DL) for robust leaf recognition. Integrating the distances obtained by dictionary learning and naive sparse representation can generate robust and high performance leaf recognition. In the fusion of distances, the number of atoms was not necessarily large as conventional methods, and even using smaller atoms produced more promising recognition at times. Therefore, not only has the leaf recognition accuracy by sparse representation been advanced, but the recognition speed also remains fast enough. A series of experiments had been conducted on five benchmark leaf datasets, including Caltech Leaves, Leaf, Herbarium, Swedish Leaf and Flavia. The experimental results demonstrated that SRLC-DL produced a higher accuracy in leaf image recognition and outperformed many other state-of-the-art methods.
KeywordSparse representation Dictionary learning Feature integration Leaf image recognition
DOI10.1016/j.compag.2017.11.013
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAgriculture ; Computer Science
WOS SubjectAgriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000419413200010
PublisherELSEVIER SCI LTD
The Source to ArticleWOS
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Citation statistics
Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Zeng, Shaoning,Zhang, Bob,Du, Yong. Joint distances by sparse representation and locality-constrained dictionary learning for robust leaf recognition[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2017,142:563-571.
APA Zeng, Shaoning,Zhang, Bob,&Du, Yong.(2017).Joint distances by sparse representation and locality-constrained dictionary learning for robust leaf recognition.COMPUTERS AND ELECTRONICS IN AGRICULTURE,142,563-571.
MLA Zeng, Shaoning,et al."Joint distances by sparse representation and locality-constrained dictionary learning for robust leaf recognition".COMPUTERS AND ELECTRONICS IN AGRICULTURE 142(2017):563-571.
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