Binary Competitive Swarm Optimizer Algorithm for Feature Selection in Identification of Chinese Fir Family
| Autor: | Dakun Lin, Yanhong Lin, Xiaolin Li, Minglin Hong, Shiguo Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Local binary patterns business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Probabilistic logic Swarm behaviour Binary number Pattern recognition Feature selection 02 engineering and technology 01 natural sciences 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 010306 general physics business Classifier (UML) |
| Zdroj: | ISPA/BDCloud/SocialCom/SustainCom |
| DOI: | 10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00220 |
| Popis: | Identification of Chinese fir family is a key procedure in forestry cultivation. However, it is time consuming depending on experts' identification. Therefore, in this paper, the method of image based recognition of Chinese fir family is proposed to replace experts' identification. For the cones of different Chinese fir families have different texture features, the textures of the cone is modeled using dominant rotated local binary patterns and these textures is selected by combination of binary competitive swarm optimization algorithm and probabilistic collaborative representation based classifier for filtered out irrelevant, noisy or redundant information. Experimental results demonstrate that the proposed approach is effective in recognizing Chinese fir family especially with feature selection. |
| Databáze: | OpenAIRE |
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