Binary Competitive Swarm Optimizer Algorithm for Feature Selection in Identification of Chinese Fir Family

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.