Discriminant WSRC for Large-Scale Plant Species Recognition

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are construct...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2017-01, Vol.2017 (2017), p.1-10
Main Authors: You, Zhuhong, Zhu, Yihai, Zhang, Chuanlei, Zhang, Shanwen
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biodiversity
Classification
Complexity
Computer applications
Data compression
Databases, Factual
Dictionaries
Flowers & plants
Humans
Identification
Image Processing, Computer-Assisted
Internet of Things
Leaves
Methods
Pattern recognition
Pattern Recognition, Automated - methods
Plants - anatomy & histology
Plants - classification
Recognition
Representations
Species
Species Specificity
Training
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Summary:In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.
ISSN:1687-5265
1687-5273
DOI:10.1155/2017/9581292